Abstract:
A method for providing cognitive insight via a cognitive information processing system comprising: encapsulating an operation for providing a desired cognitive insight; and, applying the operation to a target cognitive graph to generate a cognitive insight based

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates in general to the field of computers and similar technologies, and in particular to software utilized in this field. Still more particularly, it relates to a method, system and computer-usable medium for performing cognitive inference and learning operations. 
         [0003]    2. Description of the Related Art 
         [0004]    In general, “big data” refers to a collection of datasets so large and complex that they become difficult to process using typical database management tools and traditional data processing approaches. These datasets can originate from a wide variety of sources, including computer systems, mobile devices, credit card transactions, television broadcasts, and medical equipment, as well as infrastructures associated with cities, sensor-equipped buildings and factories, and transportation systems. Challenges commonly associated with big data, which may be a combination of structured, unstructured, and semi-structured data, include its capture, curation, storage, search, sharing, analysis and visualization. In combination, these challenges make it difficult to efficiently process large quantities of data within tolerable time intervals. 
         [0005]    Nonetheless, big data analytics hold the promise of extracting insights by uncovering difficult-to-discover patterns and connections, as well as providing assistance in making complex decisions by analyzing different and potentially conflicting options. As such, individuals and organizations alike can be provided new opportunities to innovate, compete, and capture value. 
         [0006]    One aspect of big data is “dark data,” which generally refers to data that is either not collected, neglected, or underutilized. Examples of data that is not currently being collected includes location data prior to the emergence of companies such as Foursquare or social data prior to the advent companies such as Facebook. An example of data that is being collected, but is difficult to access at the right time and place, includes data associated with the side effects of certain spider bites while on a camping trip. As another example, data that is collected and available, but has not yet been productized of fully utilized, may include disease insights from population-wide healthcare records and social media feeds. As a result, a case can be made that dark data may in fact be of higher value than big data in general, especially as it can likely provide actionable insights when it is combined with readily-available data. 
       SUMMARY OF THE INVENTION 
       [0007]    In one embodiment, the invention relates to a method for providing cognitive insights via a cognitive information processing system comprising: encapsulating an operation for providing a desired cognitive insight; and, applying the operation to a target cognitive graph to generate a cognitive insight based upon the operation. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element. 
           [0009]      FIG. 1  depicts an exemplary client computer in which the present invention may be implemented; 
           [0010]      FIG. 2  is a simplified block diagram of a cognitive inference and learning system (CILS); 
           [0011]      FIG. 3  is a simplified block diagram of a CILS reference model implemented in accordance with an embodiment of the invention; 
           [0012]      FIGS. 4   a  through  4   c  depict additional components of the CILS reference model shown in  FIG. 3 ; 
           [0013]      FIG. 5  is a simplified process diagram of CILS operations; 
           [0014]      FIG. 6  is a depicts the lifecycle of CILS agents implemented to perform CILS operations; 
           [0015]      FIG. 7  is a simplified block diagram of a plurality of cognitive platforms implemented in a hybrid cloud environment; and 
           [0016]      FIG. 8  is a simplified process flow diagram of a cognitive insight generation operations. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]    A method, system and computer-usable medium are disclosed for cognitive inference and learning operations. The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0018]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0019]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0020]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0021]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
         [0022]    These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0023]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0024]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
         [0025]      FIG. 1  is a generalized illustration of an information processing system  100  that can be used to implement the system and method of the present invention. The information processing system  100  includes a processor (e.g., central processor unit or “CPU”)  102 , input/output (I/O) devices  104 , such as a display, a keyboard, a mouse, and associated controllers, a hard drive or disk storage  106 , and various other subsystems  108 . In various embodiments, the information processing system  100  also includes network port  110  operable to connect to a network  140 , which is likewise accessible by a service provider server  142 . The information processing system  100  likewise includes system memory  112 , which is interconnected to the foregoing via one or more buses  114 . System memory  112  further comprises operating system (OS)  116  and in various embodiments may also comprise cognitive inference and learning system (CILS)  118 . In these and other embodiments, the CILS  118  may likewise comprise invention modules  120 . In one embodiment, the information processing system  100  is able to download the CILS  118  from the service provider server  142 . In another embodiment, the CILS  118  is provided as a service from the service provider server  142 . 
         [0026]    In various embodiments, the CILS  118  is implemented to perform various cognitive computing operations described in greater detail herein. As used herein, cognitive computing broadly refers to a class of computing involving self-learning systems that use techniques such as spatial navigation, machine vision, and pattern recognition to increasingly mimic the way the human brain works. To be more specific, earlier approaches to computing typically solved problems by executing a set of instructions codified within software. In contrast, cognitive computing approaches are data-driven, sense-making, insight-extracting, problem-solving systems that have more in common with the structure of the human brain than with the architecture of contemporary, instruction-driven computers. 
         [0027]    To further differentiate these distinctions, traditional computers must first be programmed by humans to perform specific tasks, while cognitive systems learn from their interactions with data and humans alike, and in a sense, program themselves to perform new tasks. To summarize the difference between the two, traditional computers are designed to calculate rapidly. Cognitive systems are designed to quickly draw inferences from data and gain new knowledge. 
         [0028]    Cognitive systems achieve these abilities by combining various aspects of artificial intelligence, natural language processing, dynamic learning, and hypothesis generation to render vast quantities of intelligible data to assist humans in making better decisions. As such, cognitive systems can be characterized as having the ability to interact naturally with people to extend what either humans, or machines, could do on their own. Furthermore, they are typically able to process natural language, multi-structured data, and experience much in the same way as humans. Moreover, they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time. 
         [0029]    It will be appreciated that more data is currently being produced every day than was recently produced by human beings from the beginning of recorded time. Deep within this ever-growing mass of data is a class of data known as “dark data,” which includes neglected information, ambient signals, and insights that can assist organizations and individuals in augmenting their intelligence and deliver actionable insights through the implementation of cognitive applications. As used herein, cognitive applications, or “cognitive apps,” broadly refer to cloud-based, big data interpretive applications that learn from user engagement and data interactions. Such cognitive applications extract patterns and insights from dark data sources that are currently almost completely opaque. Examples of such dark data include disease insights from population-wide healthcare records and social media feeds, or from new sources of information, such as sensors monitoring pollution in delicate marine environments. 
         [0030]    Over time, it is anticipated that cognitive applications will fundamentally change the ways in which many organizations operate as they invert current issues associated with data volume and variety to enable a smart, interactive data supply chain. Ultimately, cognitive applications hold the promise of receiving a user query and immediately providing a data-driven answer from a masked data supply chain in response. As they evolve, it is likewise anticipated that cognitive applications may enable a new class of “sixth sense” applications that intelligently detect and learn from relevant data and events to offer insights, predictions and advice rather than wait for commands. Just as web and mobile applications changed the way people access data, cognitive applications may change the way people listen to, and become empowered by, multi-structured data such as emails, social media feeds, doctors notes, transaction records, and call logs. 
         [0031]    However, the evolution of such cognitive applications has associated challenges, such as how to detect events, ideas, images, and other content that may be of interest. For example, assuming that the role and preferences of a given user are known, how is the most relevant information discovered, prioritized, and summarized from large streams of multi-structured data such as news feeds, blogs, social media, structured data, and various knowledge bases? To further the example, what can a healthcare executive be told about their competitor&#39;s market share? Other challenges include the creation of a contextually-appropriate visual summary of responses to questions or queries. 
         [0032]      FIG. 2  is a simplified block diagram of a cognitive inference and learning system (CILS) implemented in accordance with an embodiment of the invention. In various embodiments, the CILS  118  is implemented to incorporate a variety of processes, including semantic analysis  202 , goal optimization  204 , collaborative filtering  206 , common sense reasoning  208 , natural language processing  210 , summarization  212 , temporal/spatial reasoning  214 , and entity resolution  216  to generate cognitive insights. 
         [0033]    As used herein, semantic analysis  202  broadly refers to performing various analysis operations to achieve a semantic level of understanding about language by relating syntactic structures. In various embodiments, various syntactic structures are related from the levels of phrases, clauses, sentences and paragraphs, to the level of the body of content as a whole and to its language-independent meaning. In certain embodiments, the semantic analysis  202  process includes processing a target sentence to parse it into its individual parts of speech, tag sentence elements that are related to predetermined items of interest, identify dependencies between individual words, and perform co-reference resolution. For example, if a sentence states that the author really likes the hamburgers served by a particular restaurant, then the name of the “particular restaurant” is co-referenced to “hamburgers.” 
         [0034]    As likewise used herein, goal optimization  204  broadly refers to performing multi-criteria decision making operations to achieve a given goal or target objective. In various embodiments, one or more goal optimization  204  processes are implemented by the CILS  118  to define predetermined goals, which in turn contribute to the generation of a cognitive insight. For example, goals for planning a vacation trip may include low cost (e.g., transportation and accommodations), location (e.g., by the beach), and speed (e.g., short travel time). In this example, it will be appreciated that certain goals may be in conflict with another. As a result, a cognitive insight provided by the CILS  118  to a traveler may indicate that hotel accommodations by a beach may cost more than they care to spend. 
         [0035]    Collaborative filtering  206 , as used herein, broadly refers to the process of filtering for information or patterns through the collaborative involvement of multiple agents, viewpoints, data sources, and so forth. The application of such collaborative filtering  206  processes typically involves very large and different kinds of data sets, including sensing and monitoring data, financial data, and user data of various kinds Collaborative filtering  206  may also refer to the process of making automatic predictions associated with predetermined interests of a user by collecting preferences or other information from many users. For example, if person ‘A’ has the same opinion as a person ‘B’ for a given issue ‘x’, then an assertion can be made that person ‘A’ is more likely to have the same opinion as person ‘B’ opinion on a different issue ‘y’ than to have the same opinion on issue ‘y’ as a randomly chosen person. In various embodiments, the collaborative filtering  206  process is implemented with various recommendation engines familiar to those of skill in the art to make recommendations. 
         [0036]    As used herein, common sense reasoning  208  broadly refers to simulating the human ability to make deductions from common facts they inherently know. Such deductions may be made from inherent knowledge about the physical properties, purpose, intentions and possible behavior of ordinary things, such as people, animals, objects, devices, and so on. In various embodiments, common sense reasoning  208  processes are implemented to assist the CILS  118  in understanding and disambiguating words within a predetermined context. In certain embodiments, the common sense reasoning  208  processes are implemented to allow the CILS  118  to generate text or phrases related to a target word or phrase to perform deeper searches for the same terms. It will be appreciated that if the context of a word is better understood, then a common sense understanding of the word can then be used to assist in finding better or more accurate information. In certain embodiments, this better or more accurate understanding of the context of a word, and its related information, allows the CILS  118  to make more accurate deductions, which are in turn used to generate cognitive insights. 
         [0037]    As likewise used herein, natural language processing (NLP)  210  broadly refers to interactions with a system, such as the CILS  118 , through the use of human, or natural, languages. In various embodiments, various NLP  210  processes are implemented by the CILS  118  to achieve natural language understanding, which enables it to not only derive meaning from human or natural language input, but to also generate natural language output. 
         [0038]    Summarization  212 , as used herein, broadly refers to processing a set of information, organizing and ranking it, and then generating a corresponding summary. As an example, a news article may be processed to identify its primary topic and associated observations, which are then extracted, ranked, and then presented to the user. As another example, page ranking operations may be performed on the same news article to identify individual sentences, rank them, order them, and determine which of the sentences are most impactful in describing the article and its content. As yet another example, a structured data record, such as a patient&#39;s electronic medical record (EMR), may be processed using the summarization  212  process to generate sentences and phrases that describes the content of the EMR. In various embodiments, various summarization  212  processes are implemented by the CILS  118  to generate summarizations of content streams, which are in turn used to generate cognitive insights. 
         [0039]    As used herein, temporal/spatial reasoning  214  broadly refers to reasoning based upon qualitative abstractions of temporal and spatial aspects of common sense knowledge, described in greater detail herein. For example, it is not uncommon for a predetermined set of data to change over time. Likewise, other attributes, such as its associated metadata, may likewise change over time. As a result, these changes may affect the context of the data. To further the example, the context of asking someone what they believe they should be doing at 3:00 in the afternoon during the workday while they are at work may be quite different that asking the same user the same question at 3:00 on a Sunday afternoon when they are at home. In various embodiments, various temporal/spatial reasoning  214  processes are implemented by the CILS  118  to determine the context of queries, and associated data, which are in turn used to generate cognitive insights. 
         [0040]    As likewise used herein, entity resolution  216  broadly refers to the process of finding elements in a set of data that refer to the same entity across different data sources (e.g., structured, non-structured, streams, devices, etc.), where the target entity does not share a common identifier. In various embodiments, the entity resolution  216  process is implemented by the CILS  118  to identify significant nouns, adjectives, phrases or sentence elements that represent various predetermined entities within one or more domains. From the foregoing, it will be appreciated that the implementation of one or more of the semantic analysis  202 , goal optimization  204 , collaborative filtering  206 , common sense reasoning  208 , natural language processing  210 , summarization  212 , temporal/spatial reasoning  214 , and entity resolution  216  processes by the CILS  118  can facilitate the generation of a semantic, cognitive model. 
         [0041]    In various embodiments, the CILS  118  receives ambient signals  220 , curated data  222 , and learned knowledge, which is then processed by the CILS  118  to generate one or more cognitive graphs  226 . In turn, the one or more cognitive graphs  226  are further used by the CILS  118  to generate cognitive insight streams, which are then delivered to one or more destinations  230 , as described in greater detail herein. 
         [0042]    As used herein, ambient signals  220  broadly refer to input signals, or other data streams, that may contain data providing additional insight or context to the curated data  222  and learned knowledge  224  received by the CILS  118 . For example, ambient signals may allow the CILS  118  to understand that a user is currently using their mobile device, at location ‘x’, at time ‘y’, doing activity ‘z’. To further the example, there is a difference between the user using their mobile device while they are on an airplane versus using their mobile device after landing at an airport and walking between one terminal and another. To extend the example even further, ambient signals may add additional context, such as the user is in the middle of a three leg trip and has two hours before their next flight. Further, they may be in terminal A 1 , but their next flight is out of C 1 , it is lunchtime, and they want to know the best place to eat. Given the available time the user has, their current location, restaurants that are proximate to their predicted route, and other factors such as food preferences, the CILS  118  can perform various cognitive operations and provide a recommendation for where the user can eat. 
         [0043]    In various embodiments, the curated data  222  may include structured, unstructured, social, public, private, streaming, device or other types of data described in greater detail herein. In certain embodiments, the learned knowledge  224  is based upon past observations and feedback from the presentation of prior cognitive insight streams and recommendations. In various embodiments, the learned knowledge  224  is provided via a feedback look that provides the learned knowledge  224  in the form of a learning stream of data. 
         [0044]    As likewise used herein, a cognitive graph  226  refers to a representation of expert knowledge, associated with individuals and groups over a period of time, to depict relationships between people, places, and things using words, ideas, audio and images. As such, it is a machine-readable formalism for knowledge representation that provides a common framework allowing data and knowledge to be shared and reused across user, application, organization, and community boundaries. 
         [0045]    In various embodiments, the information contained in, and referenced by, a cognitive graph  226  is derived from many sources (e.g., public, private, social, device), such as curated data  222 . In certain of these embodiments, the cognitive graph  226  assists in the identification and organization of information associated with how people, places and things are related to one other. In various embodiments, the cognitive graph  226  enables automated agents, described in greater detail herein, to access the Web more intelligently, enumerate inferences through utilization of curated, structured data  222 , and provide answers to questions by serving as a computational knowledge engine. 
         [0046]    In certain embodiments, the cognitive graph  226  not only elicits and maps expert knowledge by deriving associations from data, it also renders higher level insights and accounts for knowledge creation through collaborative knowledge modeling. In various embodiments, the cognitive graph  226  is a machine-readable, declarative memory system that stores and learns both episodic memory (e.g., specific personal experiences associated with an individual or entity), and semantic memory, which stores factual information (e.g., geo location of an airport or restaurant). 
         [0047]    For example, the cognitive graph  226  may know that a given airport is a place, and that there is a list of related places such as hotels, restaurants and departure gates. Furthermore, the cognitive graph  226  may know that people such as business travelers, families and college students use the airport to board flights from various carriers, eat at various restaurants, or shop at certain retail stores. The cognitive graph  226  may also have knowledge about the key attributes from various retail rating sites that travelers have used to describe the food and their experience at various venues in the airport over the past six months. 
         [0048]    In certain embodiments, the cognitive insight stream  228  is bidirectional, and supports flows of information both too and from destinations  230 . In these embodiments, the first flow is generated in response to receiving a query, and subsequently delivered to one or more destinations  230 . The second flow is generated in response to detecting information about a user of one or more of the destinations  230 . Such use results in the provision of information to the CILS  118 . In response, the CILS  118  processes that information, in the context of what it knows about the user, and provides additional information to the user, such as a recommendation. In various embodiments, the cognitive insight stream  228  is configured to be provided in a “push” stream configuration familiar to those of skill in the art. In certain embodiments, the cognitive insight stream  228  is implemented to use natural language approaches familiar to skilled practitioners of the art to support interactions with a user. 
         [0049]    In various embodiments, the cognitive insight stream  228  may include a stream of visualized insights. As used herein, visualized insights broadly refers to cognitive insights that are presented in a visual manner, such as a map, an infographic, images, and so forth. In certain embodiments, these visualized insights may include various cognitive insights, such as “What happened?”, “What do I know about it?”, “What is likely to happen next?”, or “What should I do about it?” In these embodiments, the cognitive insight stream is generated by various cognitive agents, which are applied to various sources, datasets, and cognitive graphs. As used herein, a cognitive agent broadly refers to a computer program that performs a task with minimum specific directions from users and learns from each interaction with data and human users. 
         [0050]    In various embodiments, the CILS  118  delivers Cognition as a Service (CaaS). As such, it provides a cloud-based development and execution platform that allow various cognitive applications and services to function more intelligently and intuitively. In certain embodiments, cognitive applications powered by the CILS  118  are able to think and interact with users as intelligent virtual assistants. As a result, users are able to interact with such cognitive applications by asking them questions and giving them commands. In response, these cognitive applications will be able to assist the user in completing tasks and managing their work more efficiently. 
         [0051]    In these and other embodiments, the CILS  118  can operate as an analytics platform to process big data, and dark data as well, to provide data analytics through a public, private or hybrid cloud environment. As used herein, cloud analytics broadly refers to a service model wherein data sources, data models, processing applications, computing power, analytic models, and sharing or storage of results are implemented within a cloud environment to perform one or more aspects of analytics. 
         [0052]    In various embodiments, users submit queries and computation requests in a natural language format to the CILS  118 . In response, they are provided with a ranked list of relevant answers and aggregated information with useful links and pertinent visualizations through a graphical representation. In these embodiments, the cognitive graph  226  generates semantic and temporal maps to reflect the organization of unstructured data and to facilitate meaningful learning from potentially millions of lines of text, much in the same way as arbitrary syllables strung together create meaning through the concept of language. 
         [0053]      FIG. 3  is a simplified block diagram of a cognitive inference and learning system (CILS) reference model implemented in accordance with an embodiment of the invention. In this embodiment, the CILS reference model is associated with the CILS  118  shown in  FIG. 2 . As shown in  FIG. 3 , the CILS  118  includes client applications  302 , application accelerators  306 , a cognitive platform  310 , and cloud infrastructure  340 . In various embodiments, the client applications  302  include cognitive applications  304 , which are implemented to understand and adapt to the user, not the other way around, by natively accepting and understanding human forms of communication, such as natural language text, audio, images, video, and so forth. 
         [0054]    In these and other embodiments, the cognitive applications  304  possess situational and temporal awareness based upon ambient signals from users and data, which facilitates understanding the user&#39;s intent, content, context and meaning to drive goal-driven dialogs and outcomes. Further, they are designed to gain knowledge over time from a wide variety of structured, non-structured, and device data sources, continuously interpreting and autonomously reprogramming themselves to better understand a given domain. As such, they are well-suited to support human decision making, by proactively providing trusted advice, offers and recommendations while respecting user privacy and permissions. 
         [0055]    In various embodiments, the application accelerators  306  include a cognitive application framework  308 . In certain embodiments, the application accelerators  306  and the cognitive application framework  308  support various plug-ins and components that facilitate the creation of client applications  302  and cognitive applications  304 . In various embodiments, the application accelerators  306  include widgets, user interface (UI) components, reports, charts, and back-end integration components familiar to those of skill in the art. 
         [0056]    As likewise shown in  FIG. 3 , the cognitive platform  310  includes a management console  312 , a development environment  314 , application program interfaces (APIs)  316 , sourcing agents  318 , a cognitive engine  320 , destination agents  336 , and platform data  338 , all of which are described in greater detail herein. In various embodiments, the management console  312  is implemented to manage accounts and projects, along with user-specific metadata that is used to drive processes and operations within the cognitive platform  310  for a predetermined project. 
         [0057]    In certain embodiments, the development environment  314  is implemented to create custom extensions to the CILS  118  shown in  FIG. 2 . In various embodiments, the development environment  314  is implemented for the development of a custom application, which may subsequently be deployed in a public, private or hybrid cloud environment. In certain embodiments, the development environment  314  is implemented for the development of a custom sourcing agent, a custom bridging agent, a custom destination agent, or various analytics applications or extensions. 
         [0058]    In various embodiments, the APIs  316  are implemented to build and manage predetermined cognitive applications  304 , described in greater detail herein, which are then executed on the cognitive platform  310  to generate cognitive insights. Likewise, the sourcing agents  318  are implemented in various embodiments to source a variety of multi-site, multi-structured source streams of data described in greater detail herein. In various embodiments, the cognitive engine  320  includes a dataset engine  322 , a graph query engine  326 , an insight/learning engine  330 , and foundation components  334 . In certain embodiments, the dataset engine  322  is implemented to establish and maintain a dynamic data ingestion and enrichment pipeline. In these and other embodiments, the dataset engine  322  may be implemented to orchestrate one or more sourcing agents  318  to source data. Once the data is sourced, the data set engine  322  performs data enriching and other data processing operations, described in greater detail herein, and generates one or more sub-graphs that are subsequently incorporated into a target cognitive graph. 
         [0059]    In various embodiments, the graph query engine  326  is implemented to receive and process queries such that they can be bridged into a cognitive graph, as described in greater detail herein, through the use of a bridging agent. In certain embodiments, the graph query engine  326  performs various natural language processing (NLP), familiar to skilled practitioners of the art, to process the queries. In various embodiments, the insight/learning engine  330  is implemented to encapsulate a predetermined algorithm, which is then applied to a cognitive graph to generate a result, such as a cognitive insight or a recommendation. In certain embodiments, one or more such algorithms may contribute to answering a specific question and provide additional cognitive insights or recommendations. In various embodiments, two or more of the dataset engine  322 , the graph query engine  326 , and the insight/learning engine  330  may be implemented to operate collaboratively to generate a cognitive insight or recommendation. In certain embodiments, one or more of the dataset engine  322 , the graph query engine  326 , and the insight/learning engine  330  may operate autonomously to generate a cognitive insight or recommendation. 
         [0060]    The foundation components  334  shown in  FIG. 3  include various reusable components, familiar to those of skill in the art, which are used in various embodiments to enable the dataset engine  322 , the graph query engine  326 , and the insight/learning engine  330  to perform their respective operations and processes. Examples of such foundation components  334  include natural language processing (NLP) components and core algorithms, such as cognitive algorithms. 
         [0061]    In various embodiments, the platform data  338  includes various data repositories, described in greater detail herein, that are accessed by the cognitive platform  310  to generate cognitive insights. In various embodiments, the destination agents  336  are implemented to publish cognitive insights to a consumer of cognitive insight data. Examples of such consumers of cognitive insight data include target databases, business intelligence applications, and mobile applications. It will be appreciated that many such examples of cognitive insight data consumers are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention. In various embodiments, as described in greater detail herein, the cloud infrastructure  340  includes cognitive cloud management  342  components and cloud analytics infrastructure components  344 . 
         [0062]      FIGS. 4   a  through  4   c  depict additional cognitive inference and learning system (CILS) components implemented in accordance with an embodiment of the CILS reference model shown in  FIG. 3 . In this embodiment, the CILS reference model includes client applications  302 , application accelerators  306 , a cognitive platform  310 , and cloud infrastructure  340 . As shown in  FIG. 4   a , the client applications  302  include cognitive applications  304 . In various embodiments, the cognitive applications  304  are implemented natively accept and understand human forms of communication, such as natural language text, audio, images, video, and so forth. In certain embodiments, the cognitive applications  304  may include healthcare  402 , business performance  403 , travel  404 , and various other  405  applications familiar to skilled practitioners of the art. As such, the foregoing is only provided as examples of such cognitive applications  304  and is not intended to limit the intent, spirit of scope of the invention. 
         [0063]    In various embodiments, the application accelerators  306  include a cognitive application framework  308 . In certain embodiments, the application accelerators  308  and the cognitive application framework  308  support various plug-ins and components that facilitate the creation of client applications  302  and cognitive applications  304 . In various embodiments, the application accelerators  306  include widgets, user interface (UI) components, reports, charts, and back-end integration components familiar to those of skill in the art. It will be appreciated that many such application accelerators  306  are possible and their provided functionality, selection, provision and support are a matter of design choice. As such, the application accelerators  306  described in greater detail herein are not intended to limit the spirit, scope or intent of the invention. 
         [0064]    As shown in  FIGS. 4   a  and  4   b , the cognitive platform  310  includes a management console  312 , a development environment  314 , application program interfaces (APIs)  316 , sourcing agents  318 , a cognitive engine  320 , destination agents  336 , platform data  338 , and a crawl framework  452 . In various embodiments, the management console  312  is implemented to manage accounts and projects, along with management metadata  461  that is used to drive processes and operations within the cognitive platform  310  for a predetermined project. 
         [0065]    In various embodiments, the management console  312  is implemented to run various services on the cognitive platform  310 . In certain embodiments, the management console  312  is implemented to manage the configuration of the cognitive platform  310 . In certain embodiments, the management console  312  is implemented to establish the development environment  314 . In various embodiments, the management console  312  may be implemented to manage the development environment  314  once it is established. Skilled practitioners of the art will realize that many such embodiments are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0066]    In various embodiments, the development environment  314  is implemented to create custom extensions to the CILS  118  shown in  FIG. 2 . In these and other embodiments, the development environment  314  is implemented to support various programming languages, such as Python, Java, R, and others familiar to skilled practitioners of the art. In various embodiments, the development environment  314  is implemented to allow one or more of these various programming languages to create a variety of analytic models and applications. As an example, the development environment  314  may be implemented to support the R programming language, which in turn can be used to create an analytic model that is then hosted on the cognitive platform  310 . 
         [0067]    In certain embodiments, the development environment  314  is implemented for the development of various custom applications or extensions related to the cognitive platform  310 , which may subsequently be deployed in a public, private or hybrid cloud environment. In various embodiments, the development environment  314  is implemented for the development of various custom sourcing agents  318 , custom enrichment agents  425 , custom bridging agents  429 , custom insight agents  433 , custom destination agents  336 , and custom learning agents  434 , which are described in greater detail herein. 
         [0068]    In various embodiments, the APIs  316  are implemented to build and manage predetermined cognitive applications  304 , described in greater detail herein, which are then executed on the cognitive platform  310  to generate cognitive insights. In these embodiments, the APIs  316  may include one or more of a project and dataset API  408 , a cognitive search API  409 , a cognitive insight API  410 , and other APIs. The selection of the individual APIs  316  implemented in various embodiments is a matter design choice and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0069]    In various embodiments, the project and dataset API  408  is implemented with the management console  312  to enable the management of a variety of data and metadata associated with various cognitive insight projects and user accounts hosted or supported by the cognitive platform  310 . In one embodiment, the data and metadata managed by the project and dataset API  408  are associated with billing information familiar to those of skill in the art. In one embodiment, the project and dataset API  408  is used to access a data stream that is created, configured and orchestrated, as described in greater detail herein, by the dataset engine  322 . 
         [0070]    In various embodiments, the cognitive search API  409  uses natural language processes familiar to those of skill in the art to search a target cognitive graph. Likewise, the cognitive insight API  410  is implemented in various embodiments to configure the insight/learning engine  330  to provide access to predetermined outputs from one or more cognitive graph algorithms that are executing in the cognitive platform  310 . In certain embodiments, the cognitive insight API  410  is implemented to subscribe to, or request, such predetermined outputs. 
         [0071]    In various embodiments, the sourcing agents  318  may include a batch upload  414  agent, an API connectors  415  agent, a real-time streams  416  agent, a Structured Query Language (SQL)/Not Only SQL (NoSQL) databases  417  agent, a message engines  418  agent, and one or more custom sourcing  420  agents. Skilled practitioners of the art will realize that other types of sourcing agents  318  may be used in various embodiments and the foregoing is not intended to limit the spirit, scope or intent of the invention. In various embodiments, the sourcing agents  318  are implemented to source a variety of multi-site, multi-structured source streams of data described in greater detail herein. In certain embodiments, each of the sourcing agents  318  has a corresponding API. 
         [0072]    In various embodiments, the batch uploading  414  agent is implemented for batch uploading of data to the cognitive platform  310 . In these embodiments, the uploaded data may include a single data element, a single data record or file, or a plurality of data records or files. In certain embodiments, the data may be uploaded from more than one source and the uploaded data may be in a homogenous or heterogeneous form. In various embodiments, the API connectors  415  agent is implemented to manage interactions with one or more predetermined APIs that are external to the cognitive platform  310 . As an example, Associated Press® may have their own API for news stories, Expedia® for travel information, or the National Weather Service for weather information. In these examples, the API connectors  415  agent would be implemented to determine how to respectively interact with each organization&#39;s API such that the cognitive platform  310  can receive information. 
         [0073]    In various embodiments, the real-time streams  416  agent is implemented to receive various streams of data, such as social media streams (e.g., Twitter feeds) or other data streams (e.g., device data streams). In these embodiments, the streams of data are received in near-real-time. In certain embodiments, the data streams include temporal attributes. As an example, as data is added to a blog file, it is time-stamped to create temporal data. Other examples of a temporal data stream include Twitter feeds, stock ticker streams, device location streams from a device that is tracking location, medical devices tracking a patient&#39;s vital signs, and intelligent thermostats used to improve energy efficiency for homes. 
         [0074]    In certain embodiments, the temporal attributes define a time window, which can be correlated to various elements of data contained in the stream. For example, as a given time window changes, associated data may have a corresponding change. In various embodiments, the temporal attributes do not define a time window. As an example, a social media feed may not have predetermined time windows, yet it is still temporal. As a result, the social media feed can be processed to determine what happened in the last 24 hours, what happened in the last hour, what happened in the last 15 minutes, and then determine related subject matter that is trending. 
         [0075]    In various embodiments, the SQL/NoSQL databases  417  agent is implemented to interact with one or more target databases familiar to those of skill in the art. For example, the target database may include a SQL, NoSQL, delimited flat file, or other form of database. In various embodiments, the message engines  418  agent is implemented to provide data to the cognitive platform  310  from one or more message engines, such as a message queue (MQ) system, a message bus, a message broker, an enterprise service bus (ESB), and so forth. Skilled practitioners of the art will realize that there are many such examples of message engines with which the message engines  418  agent may interact and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0076]    In various embodiments, the custom sourcing agents  420 , which are purpose-built, are developed through the use of the development environment  314 , described in greater detail herein. Examples of custom sourcing agents  420  include sourcing agents for various electronic medical record (EMR) systems at various healthcare facilities. Such EMR systems typically collect a variety of healthcare information, much of it the same, yet it may be collected, stored and provided in different ways. In this example, the custom sourcing agents  420  allow the cognitive platform  310  to receive information from each disparate healthcare source. 
         [0077]    In various embodiments, the cognitive engine  320  includes a dataset engine  322 , a graph engine  326 , an insight/learning engine  330 , learning agents  434 , and foundation components  334 . In these and other embodiments, the dataset engine  322  is implemented as described in greater detail to establish and maintain a dynamic data ingestion and enrichment pipeline. In various embodiments, the dataset engine  322  may include a pipelines  422  component, an enrichment  423  component, a storage component  424 , and one or more enrichment agents  425 . 
         [0078]    In various embodiments, the pipelines  422  component is implemented to ingest various data provided by the sourcing agents  318 . Once ingested, this data is converted by the pipelines  422  component into streams of data for processing. In certain embodiments, these managed streams are provided to the enrichment  423  component, which performs data enrichment operations familiar to those of skill in the art. As an example, a data stream may be sourced from Associated Press® by a sourcing agent  318  and provided to the dataset engine  322 . The pipelines  422  component receives the data stream and routes it to the enrichment  423  component, which then enriches the data stream by performing sentiment analysis, geotagging, and entity detection operations to generate an enriched data stream. In certain embodiments, the enrichment operations include filtering operations familiar to skilled practitioners of the art. To further the preceding example, the Associated Press® data stream may be filtered by a predetermined geography attribute to generate an enriched data stream. 
         [0079]    The enriched data stream is then subsequently stored, as described in greater detail herein, in a predetermined location. In various embodiments, the enriched data stream is cached by the storage  424  component to provide a local version of the enriched data stream. In certain embodiments, the cached, enriched data stream is implemented to be “replayed” by the cognitive engine  320 . In one embodiment, the replaying of the cached, enriched data stream allows incremental ingestion of the enriched data stream instead of ingesting the entire enriched data stream at one time. In various embodiments, one or more enrichment agents  425  are implemented to be invoked by the enrichment component  423  to perform one or more enrichment operations described in greater detail herein. 
         [0080]    In various embodiments, the graph query engine  326  is implemented to receive and process queries such that they can be bridged into a cognitive graph, as described in greater detail herein, through the use of a bridging agent. In these embodiments, the graph query engine may include a query  426  component, a translate  427  component, a bridge  428  component, and one or more bridging agents  429 . 
         [0081]    In various embodiments, the query  426  component is implemented to support natural language queries. In these and other embodiments, the query  426  component receives queries, processes them (e.g., using NLP processes), and then maps the processed query to a target cognitive graph. In various embodiments, the translate  427  component is implemented to convert the processed queries provided by the query  426  component into a form that can be used to query a target cognitive graph. To further differentiate the distinction between the functionality respectively provided by the query  426  and translate  427  components, the query  426  component is oriented toward understanding a query from a user. In contrast, the translate  427  component is oriented to translating a query that is understood into a form that can be used to query a cognitive graph. 
         [0082]    In various embodiments, the bridge  428  component is implemented to generate an answer to a query provided by the translate  427  component. In certain embodiments, the bridge  428  component is implemented to provide domain-specific responses when bridging a translated query to a cognitive graph. For example, the same query bridged to a target cognitive graph by the bridge  428  component may result in different answers for different domains, dependent upon domain-specific bridging operations performed by the bridge  428  component. 
         [0083]    To further differentiate the distinction between the translate  427  component and the bridging  428  component, the translate  427  component relates to a general domain translation of a question. In contrast, the bridging  428  component allows the question to be asked in the context of a specific domain (e.g., healthcare, travel, etc.), given what is known about the data. In certain embodiments, the bridging  428  component is implemented to process what is known about the translated query, in the context of the user, to provide an answer that is relevant to a specific domain. 
         [0084]    As an example, a user may ask, “Where should I eat today?” If the user has been prescribed a particular health regimen, the bridging  428  component may suggest a restaurant with a “heart healthy” menu. However, if the user is a business traveler, the bridging  428  component may suggest the nearest restaurant that has the user&#39;s favorite food. In various embodiments, the bridging  428  component may provide answers, or suggestions, that are composed and ranked according to a specific domain of use. In various embodiments, the bridging agent  429  is implemented to interact with the bridging component  428  to perform bridging operations described in greater detail herein. In these embodiments, the bridging agent interprets a translated query generated by the query  426  component within a predetermined user context, and then maps it to predetermined nodes and links within a target cognitive graph. 
         [0085]    In various embodiments, the insight/learning engine  330  is implemented to encapsulate a predetermined algorithm, which is then applied to a target cognitive graph to generate a result, such as a cognitive insight or a recommendation. In certain embodiments, one or more such algorithms may contribute to answering a specific question and provide additional cognitive insights or recommendations. In these and other embodiments, the insight/learning engine  330  is implemented to perform insight/learning operations, described in greater detail herein. In various embodiments, the insight/learning engine  330  may include a discover/visibility  430  component, a predict  431  component, a rank/recommend  432  component, and one or more insight  433  agents. 
         [0086]    In various embodiments, the discover/visibility  430  component is implemented to provide detailed information related to a predetermined topic, such as a subject or an event, along with associated historical information. In certain embodiments, the predict  431  component is implemented to perform predictive operations to provide insight into what may next occur for a predetermined topic. In various embodiments, the rank/recommend  432  component is implemented to perform ranking and recommendation operations to provide a user prioritized recommendations associated with a provided cognitive insight. 
         [0087]    In certain embodiments, the insight/learning engine  330  may include additional components. For example the additional components may include classification algorithms, clustering algorithms, and so forth. Skilled practitioners of the art will realize that many such additional components are possible and that the foregoing is not intended to limit the spirit, scope or intent of the invention. In various embodiments, the insights agents  433  are implemented to create a visual data story, highlighting user-specific insights, relationships and recommendations. As a result, it can share, operationalize, or track business insights in various embodiments. In various embodiments, the learning agent  434  work in the background to continually update the cognitive graph, as described in greater detail herein, from each unique interaction with data and users. 
         [0088]    In various embodiments, the destination agents  336  are implemented to publish cognitive insights to a consumer of cognitive insight data. Examples of such consumers of cognitive insight data include target databases, business intelligence applications, and mobile applications. In various embodiments, the destination agents  336  may include a Hypertext Transfer Protocol (HTTP) stream  440  agent, an API connectors  441  agent, a databases  442  agent, a message engines  443  agent, a mobile push notification  444  agent, and one or more custom destination  446  agents. Skilled practitioners of the art will realize that other types of destination agents  318  may be used in various embodiments and the foregoing is not intended to limit the spirit, scope or intent of the invention. In certain embodiments, each of the destination agents  318  has a corresponding API. 
         [0089]    In various embodiments, the HTTP stream  440  agent is implemented for providing various HTTP streams of cognitive insight data to a predetermined cognitive data consumer. In these embodiments, the provided HTTP streams may include various HTTP data elements familiar to those of skill in the art. In certain embodiments, the HTTP streams of data are provided in near-real-time. In various embodiments, the API connectors  441  agent is implemented to manage interactions with one or more predetermined APIs that are external to the cognitive platform  310 . As an example, various target databases, business intelligence applications, and mobile applications may each have their own unique API. 
         [0090]    In various embodiments, the databases  442  agent is implemented for provision of cognitive insight data to one or more target databases familiar to those of skill in the art. For example, the target database may include a SQL, NoSQL, delimited flat file, or other form of database. In these embodiments, the provided cognitive insight data may include a single data element, a single data record or file, or a plurality of data records or files. In certain embodiments, the data may be provided to more than one cognitive data consumer and the provided data may be in a homogenous or heterogeneous form. In various embodiments, the message engines  443  agent is implemented to provide cognitive insight data to one or more message engines, such as a message queue (MQ) system, a message bus, a message broker, an enterprise service bus (ESB), and so forth. Skilled practitioners of the art will realize that there are many such examples of message engines with which the message engines  443  agent may interact and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0091]    In various embodiments, the custom destination agents  420 , which are purpose-built, are developed through the use of the development environment  314 , described in greater detail herein. Examples of custom destination agents  420  include destination agents for various electronic medical record (EMR) systems at various healthcare facilities. Such EMR systems typically collect a variety of healthcare information, much of it the same, yet it may be collected, stored and provided in different ways. In this example, the custom destination agents  420  allow such EMR systems to receive cognitive insight data in a form they can use. 
         [0092]    In various embodiments, data that has been cleansed, normalized and enriched by the dataset engine, as described in greater detail herein, is provided by a destination agent  336  to a predetermined destination, likewise described in greater detail herein. In these embodiments, neither the graph query engine  326  nor the insight/learning engine  330  are implemented to perform their respective functions. 
         [0093]    In various embodiments, the foundation components  334  are implemented to enable the dataset engine  322 , the graph query engine  326 , and the insight/learning engine  330  to perform their respective operations and processes. In these and other embodiments, the foundation components  334  may include an NLP core  436  component, an NLP services  437  component, and a dynamic pipeline engine  438 . In various embodiments, the NLP core  436  component is implemented to provide a set of predetermined NLP components for performing various NLP operations described in greater detail herein. 
         [0094]    In these embodiments, certain of these NLP core components are surfaced through the NLP services  437  component, while some are used as libraries. Examples of operations that are performed with such components include dependency parsing, parts-of-speech tagging, sentence pattern detection, and so forth. In various embodiments, the NLP services  437  component is implemented to provide various internal NLP services, which are used to perform entity detection, summarization, and other operations, likewise described in greater detail herein. In these embodiments, the NLP services  437  component is implemented to interact with the NLP core  436  component to provide predetermined NLP services, such as summarizing a target paragraph. 
         [0095]    In various embodiments, the dynamic pipeline engine  438  is implemented to interact with the dataset engine  322  to perform various operations related to receiving one or more sets of data from one or more sourcing agents, apply enrichment to the data, and then provide the enriched data to a predetermined destination. In these and other embodiments, the dynamic pipeline engine  438  manages the distribution of these various operations to a predetermined compute cluster and tracks versioning of the data as it is processed across various distributed computing resources. In certain embodiments, the dynamic pipeline engine  438  is implemented to perform data sovereignty management operations to maintain sovereignty of the data. 
         [0096]    In various embodiments, the platform data  338  includes various data repositories, described in greater detail herein, that are accessed by the cognitive platform  310  to generate cognitive insights. In these embodiments, the platform data  338  repositories may include repositories of dataset metadata  456 , cognitive graphs  457 , models  459 , crawl data  460 , and management metadata  461 . In various embodiments, the dataset metadata  456  is associated with curated data  458  contained in the repository of cognitive graphs  457 . In these and other embodiments, the repository of dataset metadata  456  contains dataset metadata that supports operations performed by the storage  424  component of the dataset engine  322 . For example, if a Mongo® NoSQL database with ten million items is being processed, and the cognitive platform  310  fails after ingesting nine million of the items, then the dataset metadata  456  may be able to provide a checkpoint that allows ingestion to continue at the point of failure instead restarting the ingestion process. 
         [0097]    Those of skill in the art will realize that the use of such dataset metadata  456  in various embodiments allows the dataset engine  322  to be stateful. In certain embodiments, the dataset metadata  456  allows support of versioning. For example versioning may be used to track versions of modifications made to data, such as in data enrichment processes described in greater detail herein. As another example, geotagging information may have been applied to a set of data during a first enrichment process, which creates a first version of enriched data. Adding sentiment data to the same million records during a second enrichment process creates a second version of enriched data. In this example, the dataset metadata stored in the dataset metadata  456  provides tracking of the different versions of the enriched data and the differences between the two. 
         [0098]    In various embodiments, the repository of cognitive graphs  457  is implemented to store cognitive graphs generated, accessed, and updated by the cognitive engine  320  in the process of generating cognitive insights. In various embodiments, the repository of cognitive graphs  457  may include one or more repositories of curated data  458 , described in greater detail herein. In certain embodiments, the repositories of curated data  458  includes data that has been curated by one or more users, machine operations, or a combination of the two, by performing various sourcing, filtering, and enriching operations described in greater detail herein. In these and other embodiments, the curated data  458  is ingested by the cognitive platform  310  and then processed, as likewise described in greater detail herein, to generate cognitive insights. In various embodiments, the repository of models  459  is implemented to store models that are generated, accessed, and updated by the cognitive engine  320  in the process of generating cognitive insights. As used herein, models broadly refer to machine learning models. In certain embodiments, the models include one or more statistical models. 
         [0099]    In various embodiments, the crawl framework  452  is implemented to support various crawlers  454  familiar to skilled practitioners of the art. In certain embodiments, the crawlers  454  are custom configured for various target domains. For example, different crawlers  454  may be used for various travel forums, travel blogs, travel news and other travel sites. In various embodiments, data collected by the crawlers  454  is provided by the crawl framework  452  to the repository of crawl data  460 . In these embodiments, the collected crawl data is processed and then stored in a normalized form in the repository of crawl data  460 . The normalized data is then provided to SQL/NoSQL database  417  agent, which in turn provides it to the dataset engine  322 . In one embodiment, the crawl database  460  is a NoSQL database, such as Mongo®. 
         [0100]    In various embodiments, the repository of management metadata  461  is implemented to store user-specific metadata used by the management console  312  to manage accounts (e.g., billing information) and projects. In certain embodiments, the user-specific metadata stored in the repository of management metadata  461  is used by the management console  312  to drive processes and operations within the cognitive platform  310  for a predetermined project. In various embodiments, the user-specific metadata stored in the repository of management metadata  461  is used to enforce data sovereignty. It will be appreciated that many such embodiments are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0101]    Referring now to  FIG. 4   c , the cloud infrastructure  340  may include a cognitive cloud management  342  component and a cloud analytics infrastructure  344  component in various embodiments. Current examples of a cloud infrastructure  340  include Amazon Web Services (AWS®), available from Amazon.com® of Seattle, Wash., IBM® Softlayer, available from International Business Machines of Armonk, N.Y., and Nebula/Openstack, a joint project between Raskspace Hosting®, of Windcrest, Tex., and the National Aeronautics and Space Administration (NASA). In these embodiments, the cognitive cloud management  342  component may include a management playbooks  468  sub-component, a cognitive cloud management console  469  sub-component, a data console  470  sub-component, an asset repository  471  sub-component. In certain embodiments, the cognitive cloud management  342  component may include various other sub-components. 
         [0102]    In various embodiments, the management playbooks  468  sub-component is implemented to automate the creation and management of the cloud analytics infrastructure  344  component along with various other operations and processes related to the cloud infrastructure  340 . As used herein, “management playbooks” broadly refers to any set of instructions or data, such as scripts and configuration data, that is implemented by the management playbooks  468  sub-component to perform its associated operations and processes. 
         [0103]    In various embodiments, the cognitive cloud management console  469  sub-component is implemented to provide a user visibility and management controls related to the cloud analytics infrastructure  344  component along with various other operations and processes related to the cloud infrastructure  340 . In various embodiments, the data console  470  sub-component is implemented to manage platform data  338 , described in greater detail herein. In various embodiments, the asset repository  471  sub-component is implemented to provide access to various cognitive cloud infrastructure assets, such as asset configurations, machine images, and cognitive insight stack configurations. 
         [0104]    In various embodiments, the cloud analytics infrastructure  344  component may include a data grid  472  sub-component, a distributed compute engine  474  sub-component, and a compute cluster management  476  sub-component. In these embodiments, the cloud analytics infrastructure  344  component may also include a distributed object storage  478  sub-component, a distributed full text search  480  sub-component, a document database  482  sub-component, a graph database  484  sub-component, and various other sub-components. In various embodiments, the data grid  472  sub-component is implemented to provide distributed and shared memory that allows the sharing of objects across various data structures. One example of a data grid  472  sub-component is Redis, an open-source, networked, in-memory, key-value data store, with optional durability, written in ANSI C. In various embodiments, the distributed compute engine  474  sub-component is implemented to allow the cognitive platform  310  to perform various cognitive insight operations and processes in a distributed computing environment. Examples of such cognitive insight operations and processes include batch operations and streaming analytics processes. 
         [0105]    In various embodiments, the compute cluster management  476  sub-component is implemented to manage various computing resources as a compute cluster. One such example of such a compute cluster management  476  sub-component is Mesos/Nimbus, a cluster management platform that manages distributed hardware resources into a single pool of resources that can be used by application frameworks to efficiently manage workload distribution for both batch jobs and long-running services. In various embodiments, the distributed object storage  478  sub-component is implemented to manage the physical storage and retrieval of distributed objects (e.g., binary file, image, text, etc.) in a cloud environment. Examples of a distributed object storage  478  sub-component include Amazon S3®, available from Amazon.com of Seattle, Wash., and Swift, an open source, scalable and redundant storage system. 
         [0106]    In various embodiments, the distributed full text search  480  sub-component is implemented to perform various full text search operations familiar to those of skill in the art within a cloud environment. In various embodiments, the document database  482  sub-component is implemented to manage the physical storage and retrieval of structured data in a cloud environment. Examples of such structured data include social, public, private, and device data, as described in greater detail herein. In certain embodiments, the structured data includes data that is implemented in the JavaScript Object Notation (JSON) format. One example of a document database  482  sub-component is Mongo, an open source cross-platform document-oriented database. In various embodiments, the graph database  484  sub-component is implemented to manage the physical storage and retrieval of cognitive graphs. One example of a graph database  484  sub-component is GraphDB, an open source graph database familiar to those of skill in the art. 
         [0107]      FIG. 5  is a simplified process diagram of cognitive inference and learning system (CILS) operations performed in accordance with an embodiment of the invention. In various embodiments, these CILS operations may include a perceive  506  phase, a relate  508  phase, an operate  510  phase, a process and execute  512  phase, and a learn  514  phase. In these and other embodiments, the CILS  118  shown in  FIG. 2  is implemented to mimic cognitive processes associated with the human brain. In various embodiments, the CILS operations are performed through the implementation of a cognitive platform  310 , described in greater detail herein. In these and other embodiments, the cognitive platform  310  may be implemented within a cloud analytics infrastructure  344 , which in turn is implemented within a cloud infrastructure  340 , likewise described in greater detail herein. 
         [0108]    In various embodiments, multi-site, multi-structured source streams  504  are provided by sourcing agents, as described in greater detail herein. In these embodiments, the source streams  504  are dynamically ingested in real-time during the perceive  506  phase, and based upon a predetermined context, extraction, parsing, and tagging operations are performed on language, text and images contained in the source streams  504 . Automatic feature extraction and modeling operations are then performed with the previously processed source streams  504  during the relate  508  phase to generate queries to identify related data (i.e., corpus expansion). 
         [0109]    In various embodiments, operations are performed during the operate  510  phase to discover, summarize and prioritize various concepts, which are in turn used to generate actionable recommendations and notifications associated with predetermined plan-based optimization goals. The resulting actionable recommendations and notifications are then processed during the process and execute  512  phase to provide cognitive insights, such as recommendations, to various predetermined destinations and associated application programming interfaces (APIs)  524 . 
         [0110]    In various embodiments, features from newly-observed data are automatically extracted from user feedback during the learn  514  phase to improve various analytical models. In these embodiments, the learn  514  phase includes feedback on observations generated during the relate  508  phase, which is provided to the perceive  506  phase. Likewise, feedback on decisions resulting from operations performed during the operate  510  phase, and feedback on results resulting from operations performed during the process and execute  512  phase, are also provided to the perceive  506  phase. 
         [0111]    In various embodiments, user interactions result from operations performed during the process and execute  512  phase. In these embodiments, data associated with the user interactions are provided to the perceive  506  phase as unfolding interactions  522 , which include events that occur external to the CILS operations described in greater detail herein. As an example, a first query from a user may be submitted to the CILS system, which in turn generates a first cognitive insight, which is then provided to the user. In response, the user may respond by providing a first response, or perhaps a second query, either of which is provided in the same context as the first query. The CILS receives the first response or second query, performs various CILS operations, and provides the user a second cognitive insight. As before, the user may respond with a second response or a third query, again in the context of the first query. Once again, the CILS performs various CILS operations and provides the user a third cognitive insight, and so forth. In this example, the provision of cognitive insights to the user, and their various associated responses, results in unfolding interactions  522 , which in turn result in a stateful dialog that evolves over time. Skilled practitioners of the art will likewise realize that such unfolding interactions  522 , occur outside of the CILS operations performed by the cognitive platform  310 . 
         [0112]      FIG. 6  depicts the lifecycle of CILS agents implemented in accordance with an embodiment of the invention to perform CILS operations. In various embodiments, the CILS agents lifecycle  602  may include implementation of a sourcing  318  agent, an enrichment  425  agent, a bridging  429  agent, an insight  433  agent, a destination  336  agent, and a learning  434  agent. In these embodiments, the sourcing  318  agent is implemented to source a variety of multi-site, multi-structured source streams of data described in greater detail herein. These sourced data streams are then provided to an enrichment  425  agent, which then invokes an enrichment component to perform enrichment operations to generate enriched data streams, likewise described in greater detail herein. 
         [0113]    The enriched data streams are then provided to a bridging  429  agent, which is used to perform bridging operations described in greater detail herein. In turn, the results of the bridging operations are provided to an insight  433  agent, which is implemented as described in greater detail herein to create a visual data story, highlighting user-specific insights, relationships and recommendations. The resulting visual data story is then provided to a destination  336  agent, which is implemented to publish cognitive insights to a consumer of cognitive insight data, likewise as described in greater detail herein. In response, the consumer of cognitive insight data provides feedback to a learning  434  agent, which is implemented as described in greater detail herein to provide the feedback to the sourcing agent  318 , at which point the CILS agents lifecycle  602  is continued. From the foregoing, skilled practitioners of the art will recognize that each iteration of the cognitive agents lifecycle  602  provides more informed cognitive insights. 
         [0114]      FIG. 7  is a simplified block diagram of a plurality of cognitive platforms implemented in accordance with an embodiment of the invention within a hybrid cloud infrastructure. In this embodiment, the hybrid cloud infrastructure  740  includes a cognitive cloud management  342  component, a hosted cognitive cloud  704  environment, and a private network  706  environment. As shown in  FIG. 7 , the hosted cognitive cloud  704  environment includes a hosted cognitive platform  710 , such as the cognitive platform  310  shown in  FIGS. 3 and 4   a  through  4   b . In various embodiments, the hosted cognitive cloud  704  environment may also include one or more repositories of curated public data sources  714  and licensed data sources  716 . Likewise, the hosted cognitive platform  710  may also include a cloud analytics infrastructure  712 , such as the cloud analytics infrastructure  344  shown in  FIGS. 3 and 4   c.    
         [0115]    As likewise shown in  FIG. 7 , the private network  706  environment includes a private cognitive platform  720 , such as the cognitive platform  310  shown in  FIGS. 3 and 4   a  through  4   b . In various embodiments, the private network cognitive cloud  706  environment may also include one or more repositories of application data  724  and private data  726 . Likewise, the private cognitive platform  720  may also include a cloud analytics infrastructure  722 , such as the cloud analytics infrastructure  344  shown in  FIGS. 3 and 4   c . In certain embodiments, the private network  706  environment may have one or more private applications  728  implemented to interact with the private cognitive platform  720 . 
         [0116]    In various embodiments, a secure tunnel  730 , such as a virtual private network (VPN) tunnel, is implemented to allow the hosted cognitive platform  710  and the on-site cognitive platform  722  to communicate with one another. In these embodiments, the ability to communicate with one another allows the hosted cognitive platform  710  and the private cognitive platform  720  to work collaboratively when generating cognitive insights described in greater detail herein. In various embodiments, the hosted cognitive platform accesses the repositories of application data  724  and private data  726  to generate various cognitive insights, which are then provided to the private cognitive platform  720 . In certain embodiments, data stored in the repositories of application data  724  and private data  726  is provided  732  to the private cognitive platform  720  in the form of public data and cognitive graphs. 
         [0117]    In various embodiments, the private cognitive platform  720  accesses the repositories of application data  724  and private data  726  to generate various cognitive insights, which are then provided to the one or more private applications  728 . In certain embodiments, the private cognitive platform  720  uses the public data and cognitive graphs provided  732  by the hosted cognitive platform  710  to generate various cognitive insights, which a then provided to the one or more private applications  728 . In various embodiments, the private cognitive platform  720  accesses the repositories of application data  724  and private data  726 , as well as uses the public data and cognitive graphs provided  732  by the hosted cognitive platform  710  to generate various cognitive insights. Once generated, the cognitive insights are then provided to the one or more private applications  728 . Skilled practitioners of the art will recognize that many such embodiments are possible and the foregoing is not intended to limit the spirit, scope or intent of the invention. 
         [0118]    In various embodiments, the private network  706  is implemented and managed by a travel industry entity, such as an airline, hotel chain, automobile rental company, or travel agency. In these embodiments, the private cognitive platform  720  is likewise implemented and managed by the travel industry entity to perform various cognitive insight operations relevant to travel activities. In certain embodiments, the private cognitive platform  720  is implemented to access travel-industry-specific application data  724  and private data  724  as described in greater detail herein. In these embodiments, the travel-industry-related application data  724  and private data  724  is specific to the travel industry entity. In one embodiment, the travel-industry-related application data  724  and private data  724  is private to the travel industry entity. 
         [0119]      FIG. 8  is a simplified process flow diagram of a cognitive insight generation operations performed in accordance with an embodiment of the invention. In various embodiments, cognitive insight operations may be performed in various phases. In this embodiment, these phases include a data lifecycle  840  phase, a learning  838  phase, and an application/insight composition  840  phase. 
         [0120]    In the data lifecycle  836  phase, a predetermined cognitive platform  810  instantiation sources social data  812 , public data, licensed data  816 , and proprietary data  818  from various sources as described in greater detail herein. In various embodiments, an example of a cognitive platform  810  instantiation is the cognitive platform  310  shown in  FIGS. 3 and 4   a  through  4   b . In this embodiment, the cognitive platform  810  instantiation includes a source  806  component, a process  808  component, a deliver  810  component, a cleanse  820  component, an enrich  822  component, a filter/transform  824  component, and a repair/reject  826  component. Likewise, as shown in  FIG. 8 , the process  808  component includes a repository of models  828 , described in greater detail herein. 
         [0121]    In various embodiments, the process  806  component is implemented to perform various cognitive insight generation and other processing operations, described in greater detail herein. In these embodiments, the process component is implemented to interact with the source  806  component, which in turn is implemented to perform various data sourcing operations described in greater detail herein. In various embodiments, the sourcing operations are performed by one or more sourcing agents, as likewise described in greater detail herein. The resulting sourced data is then provided to the process  808  component. In turn, the process  808  component is implemented to interact with the cleanse  820  component, which is implemented to perform various data cleansing operations familiar to those of skill in the art. As an example, the cleanse  820  component may perform data normalization or pruning operations, likewise known to skilled practitioners of the art. In certain embodiments, the cleanse  820  component may be implemented to interact with the repair/reject  826  component, which in turn is implemented to perform various data repair or data rejection operations known to those of skill in the art. 
         [0122]    Once data cleansing, repair and rejection operations are completed, the process  808  component is implemented to interact with the enrich  822  component, which is implemented to perform various data enrichment operations described in greater detail herein. Once data enrichment operations have been completed, the process  808  component is likewise implemented to interact with the filter/transform  824 , which in turn is implemented to perform data filtering and transformation operations described in greater detail. 
         [0123]    In various embodiments, the process  808  component is implemented to generate various models, described in greater detail herein, which are stored in the repository of models  828 . The process  808  component is likewise implemented in various embodiments use the sourced data to generate one or more cognitive graphs  226 , as described in greater detail herein. In various embodiments, the process  808  component is implemented to gain an understanding of the data sourced from the sources of social data  812 , public data, licensed data  816 , and proprietary data  818 , which assist in the automated generation of the cognitive graph  226 . 
         [0124]    The process  808  component is likewise implemented in various embodiments to perform bridging  846  operations, likewise described in greater detail, to access the cognitive graph  226 . In certain embodiments, the bridging  846  operations are performed by bridging agents, as described in greater detail herein. In various embodiments, the cognitive graph  226  is accessed by the process  808  component during the learning  836  phase of the cognitive insight generation operations. 
         [0125]    In various embodiments, a cognitive application  304  is implemented to receive user input, such as a user query  842 , which is then submitted during the application/insight composition  840  phase to a graph query engine  326 . In turn, the graph query engine  326  processes the user query  842  to generate a graph query  844 , as described in greater detail herein. The graph query  844  is then used to query the cognitive graph  226 , which results in the generation of one or more cognitive insights. In various embodiments, the process  808  component is implemented to provide these cognitive insights to the deliver  810 , which in turn is implemented to deliver the cognitive insights in the form of a visual data summary  848  to the cognitive application  304 . In various embodiments, as described in the descriptive text associated with  FIG. 5 , learning operations are iteratively performed during the learning  838  phase to provide more accurate and useful cognitive insights. 
         [0126]    In various embodiments, the cognitive insight generation operations are performed to generate travel-relevant cognitive insights. In these embodiments the social data  812 , public data, licensed data  816 , and proprietary data  818  sourced from various sources may contain travel-relevant data. For example, the licensed data  816  may be ticket sale information from Sojurn®, weather data from Weather Underground®, Weather.com®, and so forth. Likewise, public data  814  may be Department of Transportation (DOT), Bureau of Transportation Services (BTS), of on-time arrival information provided by various airlines. Proprietary data  818  may likewise include data privately-owned data, such as an airline&#39;s frequent flier information that is only used internally to the airline. 
         [0127]    As described in greater detail herein, the cognitive platform  810  instantiation is implemented in these embodiments to process this travel-relevant data, and other associated data, to generate travel-relevant cognitive insights. As an example, a user may provide a travel-relevant user query  842  to a travel website, such as TripAdvisor.com. In this example, the cognitive insight generation operations are performed to provide an enhanced cognitive search of the travel-relevant website to find a preferred destination, for a specific time frame, for the user. To extend the example, the travel-relevant user query  842  may not be in the form of a traditional query. Instead, the user may submit a statement, such as, “I want to go on a vacation with my family, to the beach, in Florida, in July.” or possibly, “I want to go to Utah in May on a mountain biking trip.” To extend the example further, the user may also state, “I want to use my frequent flier miles for airline travel and my awards program points for my accommodations.” 
         [0128]    In various embodiments, a user query  842  that includes such statements is processed by the graph query engine  326  to generate one or more travel-relevant graph queries  844 . In these embodiments, these travel-relevant graph queries  844  are implemented to understand concepts like destinations, travel-related activities, and purpose of travel. Examples of such concepts include the difference between a honeymoon and a business trip, time frames that are related to travel (e.g., flight segments, time zones, etc.), and various recreational venues. 
         [0129]    The resulting graph queries  844  are then used to query a travel-relevant instantiation of the cognitive graph  226 , which in turn results in the generation of one or more travel-relevant cognitive insights. In certain of these embodiments, the cognitive graph  226  contains travel-relevant data, such as locations, hotels, prices, promotions, and so forth. In various embodiments, the deliver  810  component is implemented to provide the travel-relevant cognitive insights in the form of a visual data summary  848 . As an example, the visual data summary  848  may be provided to the user as a travel review. In various embodiments, the visual data summary  848  may be provided to a predetermined destination associated with the user. In these embodiments, the destination may be a mobile application, an alert, a business intelligence application, a statistical tool, a third party application, a marketplace, or an application program interface (API). 
         [0130]    Although the present invention has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims.