Method and system for supporting inductive reasoning queries over multi-modal data from relational databases

A system and a method for performing queries, including generating text representations of features of various types of data, building a multi-modal word embedding model to capture relationships between the various types of data, and based on the multi-modal word embedding model, performing an inductive reasoning query.

BACKGROUND OF THE INVENTION

Field of the Invention

The disclosed invention relates generally to an embodiment of a method and system for supporting queries, and more particularly, but not by way of limitation, relating to a method and system for supporting inductive reasoning queries over multi-modal data from relational databases.

Description of the Background Art

In today's world of the Internet, modern electronics, business, finance, healthcare, and other venues, there are massive amounts of data which are difficult to manage and use. With the rise of Artificial Intelligence, there is a tool to help manage and use the received information, but practical applications have had problems. There is a problem of using the large amount of information in a cognitive manner especially with multi-modal data.

In broad terms, cognition refers to the process of building knowledge capabilities using innate resources (i.e., intelligence), enriching it with external inputs such as experiences or interactions, and applying the knowledge to solve problems that in turn, feeds back towards knowledge building. While these definitions are more relevant to animate objects, they can be also applicable to scenarios in which inanimate entities simulate cognitive processes.

Therefore, there is a need to use cognitive processes in order to more efficiently manage and use the large amounts of data that are of various types in order to enable artificial intelligence capabilities.

SUMMARY OF INVENTION

In view of the foregoing and other problems, disadvantages, and drawbacks of the aforementioned background art, an exemplary aspect of the disclosed invention provides a method and system for supporting inductive reasoning queries over multi-modal data from relational databases.

One aspect of the present invention is to provide a method of performing queries, including generating text representations of features of various types of data, building a multi-modal word embedding model to capture relationships between the various types of data, and based on the multi-modal word embedding model, performing an inductive reasoning query.

Another aspect of the present invention provides system for performing queries, including a memory storing computer instructions, and a processor configured to execute the computer instructions to generate text representations of features of various types of data, and build a multi-modal word embedding model to capture relationships between the various types of data, and based on the multi-modal word embedding model, perform an inductive reasoning query.

Another example aspect of the disclosed invention is to provide a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and executable by a computer to cause the computer to perform a method, including generating text representations of features of various types of data, and building a multi-modal word embedding model to capture relationships between the various types of data, and based on the multi-modal word embedding model, performing an inductive reasoning query.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

There is disclosed Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. This model captures the hidden inter-/intra-column relationships between database tokens of different types. For each database token, the model includes a vector that encodes contextual semantic relationships. The system seamlessly integrates the word embedding model into existing SQL (Structured Query Language) query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries. CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. This system exemplifies using AI functionality to endow relational databases with capabilities that were previously very hard to realize in practice.

In the system, there is a focus on a particular cognitive process of reading comprehension of text via contexts and apply it to relational databases. There is an objective to build a cognitive relational database system that not only extracts latent semantic information, but can also enrich it by using external input (e.g., external knowledge bases, new data being inserted, or types of invoked queries) and use it transparently to enhance its query capabilities.

The following a word embedding overview. There is unsupervised neural network-based NLP approach to capture meanings of words-based neighborhood context. Meaning is captured as collective contributions from words in the neighborhood

Word embedding generates semantic representation of words as low-dimensional vectors (200-300 dimensions). Semantic similarity measured using distance metric (e.g., cosine distance) between vectors

Some of the features of Cognitive database is as follows. The Cognitive database uses dual view of relational data: tables and meaningful text, with all relational entities mapped to text, without loss of information. The Cognitive database uses word-embedding approach to extract latent information from database tables. Classical Word embedding model is extended to capture constraints of the relational model (e.g., primary keys). The database also enables relational databases to capture and exploit semantic contextual similarities.

FIG. 1illustrates the embedding model. In the Embedding model External Unstructured and Structured Data sources110are inputted as a pre-trained model into the word embedding model108. Structured data sources relational tables106are also inputted into the word embedding model108. The model108is built from data sources being queried. Cognitive intelligence queries in structured query systems104receive an output from the word embedding model108. The Cognitive intelligence queries in structured query systems104uses and updates the Structured data sources relational tables106and the structured results relational tables102.

In the database context, vectors may be produced by either learning on text transformed and extracted from the database itself and/or using external text sources. For learning from a database, a natural way of generating vectors is to apply the word embedding method to a string of tokens generated from the database: each row (tuple) would correspond to a sentence and a relation would correspond to a document. Thus, vectors enable a dual view of the data: relational and meaningful text.

The cognitive database features include enabling SQL-based information retrieval based on semantic context, rather than, data values. Unlike analytics databases, the cognitive database does not view database tables as feature and model repositories. The cognitive database has latent features exposed to users via standard SQL based Cognitive Intelligence (CI) queries. Moreover, in cognitive database, users can invoke standard SQL queries using typed relational variables over a semantic model built over untyped strings.

To illustrate this process, considerFIG. 2that present a simple customer sales table.FIG. 2shows an English sentence like representation of the fourth row206in the table202(note that the numeric value 25 is represented by the string cluster_10 for the amount of the crayons, folders in the category stationary) Using the scope of the generated sentence as the context, the word embedding approach infers latent semantic information in terms of token associations and co-occurrences and encode it in vectors. Thus, the vectors capture first inter- and intra-column relationships within a row (sentence) and then aggregate these relationships across the relation (document) to compute the collective semantic relationships. At the end of training, each unique token in the database would be associated with a d-dimensional meaning vector, which can be then used to query the source database.

In this example, the relational entity custD (customer D) is semantically similar to custB (customer B) due to many common semantic contributors (e.g., Merchant_B, Stationery, and Crayons). Equivalently, custA (customer A) is similar to custC (customer C) due to similar reasons. One may use a relational view (i.e., a SQL query) of a table, rather than the original table, to generate text representing the database content. The text representation of the table row is shown in204with “custD 9/16 Store D NY Stationery ‘Crayons, Folders’ 25”.

This example illustrates a feature of the cognitive database: the neighborhood context used for building the word embedding model is determined by the relational view being used. Hence, the inferred semantic meaning of the relational entities reflects the collective relationships defined within the associated relational view.

The cognitive relational database has been designed as an extension to the underlying relational database, and thus supports all existing relational features. A cognitive relational database supports a new class of business intelligence (BI) queries called Cognitive Intelligence (CI) queries. CI queries take relations as input, use the word embedding vectors to enable novel semantics queries over the relational data, and return a relation as output. CI queries augment the capabilities of the traditional relational BI queries by providing contextual semantics-based insights and can be used in conjunction with existing SQL operators.

The system trains a word embedding model using data from a relational database. The training approach is characterized by two unique aspects: (1) Using unstructured text representation of the structured relational data as input to the training process, and (2) Using the unsupervised word embedding technique to generate meaning vectors from the input text corpus.

With reference toFIG. 3(and alsoFIG. 1), The data is prepared as follows. The data preparation stage takes a relational table with different SQL types as input and returns an unstructured but meaningful text corpus consisting of a set of sentences302. This transformation allows us to generate a uniform semantic representation of different SQL types. This process of textification requires two stages: data pre-processing304and text conversion (FIG. 3).

The textification phase processes each relational row separately and for each row, converts data of different SQL data types to text. In some scenarios, one may want to build a model that also captures relational column names. For such cases, the pre-processing stage304first processes the column names before processing the corresponding data.

In addition to text tokens, the implementation supports numeric values and images (one assumes that the database being queried contains a VARCHAR column storing links to the images). These techniques can be applied to other SQL datatypes such as SQL Date as well. For numeric values, the system three different approaches to generate equivalent text representations:(1) creating a string version of the numerical value, e.g., value 100.0 for the column name price can be represented by either PRICE_100.0 or “100.0”,(2) User-managed categorization: a user can specify rules to define ranges for the numeric values and use them to generate string tokens for the numeric values. For ex-ample, consider values for a column name, Cocoa Contents. The value 80%, can be replaced by the string token choc_dark, while the value 35%, can be replaced by the string token choc_med, etc., and(3) user-directed clustering: a user can choose values of one or more numerical columns and cluster them using traditional clustering algorithms such as K-Means. Each numeric value is then replaced by a string representing the cluster in which that value lies (e.g., cluster_10 for value 25 inFIG. 2).

For image data, approaches similar to ones used for numerical values can be used. The first approach represents an image by its string token, e.g., a string representing the image path or a unique identifier. The second approach uses pre-existing classifiers to cluster images into groups and then uses the cluster information as the string representation of the image. For example, one can use a domain-specific deep neural network (DNN) based classifier to cluster input images into classes and then use the corresponding class information to create the string identifiers for the images. The final approach applies of-the-shelf image to tag generators, e.g., IBM Watson Visual Recognition System (VRS), to extract image features and uses them as string identifiers for an image. For example, a Lion image can be represented by the following string features, Animal, Mammal, Carnivore, BigCat, Yellow, etc.

Once text, numeric values and images are replaced by their text representations, a relational table can be viewed as an unstructured meaningful text corpus306to be used for building a word embedding model308. For Null values of these types, the system replaces them by the string column_name_Null. The methods outlined here can be applied to other data types such as SQL Date and spatial data types such as latitude and longitude.

Training the model is further clarified as follows. The system can use an unsupervised neural network approach, based on the Word2Vec (W2V) implementation, to build the word embedding model from the relational database data. The present training approach operates on the unstructured text corpus, organized as a collection of English-like sentences, separated by stop words (e.g., newline). A text token in a training set can represent either text, numeric, or image data. Thus, the model builds a joint latent representation that integrates information across different modalities using untyped uniform feature (or meaning) vectors.

Some of key differences from previous approaches are as follows. A sentence generated from a relational row is generally not in any controlled natural language such as English.1 Therefore, W2V's assumption that the influence of any word on a nearby word decreases as the word distances increases, is not applicable. In the present implementation, every token in the training set has the same influence on the nearby tokens; i.e., we view the generated sentence as a bag of words, rather than an ordered sequence.

Another consequence is that unlike an English sentence, the last word is equally related to the first word as to its other neighbors. To enable such relationships, we use a circular neighborhood window that wraps around a sentence (i.e., for the last word, the first word can be viewed as its immediate neighbor).

After training, meaning of an English token, e.g., Banana, no longer corresponds to its original English meaning, but represents semantic contributions of tokens from the sentences corresponding to the relational rows.

For relational data, the system provides special consideration to primary keys, which are unique. First, the classical W2V discards less frequent words from computations. In the present implementation, every token, irrespective of its frequency, is assigned a vector. Second, irrespective of the distance, a primary key is considered a neighbor of every other word in a sentence and included in the neighborhood window for each word. Also, the neighborhood extends via foreign key occurrences of a key value to the row in which that value is key.

Finally, the present implementation is designed to enable incremental training, i.e., the training system takes as input a pre-trained model and a new set of generated sentences, and returns an updated model. This capability is critical as a database can be updated regularly and one cannot rebuild the model from scratch every time. The pre-trained model310can be built from the database being queried, or from an external source. Such sources may be publicly available general sources (e.g., WIKIPEDIA), text from a specific domain (e.g., from the FDA regarding medical drugs), text textified from other databases or text formed from a different subset of tables of the same database. The use of pre-trained models310is an example of transfer learning, where a model trained on an external knowledge base can be used either for querying purposes or as a basis of a new model. The trained model312is thus generated.

In practice, enterprise database systems, as well as data warehouses, are built using many inter-related database tables. The training approach outlined here can be extended to enable training over multiple tables.

The cognitive database system is built as follows.FIG. 4illustrates the cognitive database stages of the exemplary embodiment. Referring toFIG. 4, Cognitive intelligence (CI) queries416are standard SQL queries and can be implemented using the existing SQL query execution infrastructure. The distinguishing aspect of cognitive intelligence queries, contextual semantic comparison between relational variables, is implemented using user-defined functions (UDFs)414. These UDFs, termed cognitive UDFs, take typed relational values as input and compute semantic relationships between them using uniformly untyped meaning vectors. This enables the relational database system to seamlessly analyze data of different types (e.g., text, numeric values, and images) using the same SQL CI query.

A cognitive UDF414takes as input either relational query variables or constant tokens, and returns a numeric similarity value that measures the semantic relationships between the input parameters. The UDFs414perform three key tasks: (1) processing input relational variables to re-create tokens used for training. This involves potentially repeating the steps executed during the data preparation stage, such as creating compound tokens. For numeric values, one can use the centroid information to identify the corresponding clusters. For images, the UDF uses the image name to obtain corresponding text tokens, (2) Once the training tokens are extracted, the UDF uses them to fetch corresponding meaning vectors from the pre-trained model, and (3) Finally, the UDF uses the fetched vectors to execute similarity computations to generate the final semantic relationship score.

The basic cognitive UDF operates on a pair of sets (or sequences) of tokens associated with the input relational parameters (note: value of a relational parameter can be a set, e.g., {Bananas, Apples}, seeFIG. 1). The core computational operation of a cognitive UDF is to calculate similarity between a pair of tokens by computing the cosine distance between the corresponding vectors.

The cognitive intelligence queries are further described as follows. The basic UDF and its extensions are invoked by the SQL CI queries to enable semantic operations on relational variables. Each CI query416uses the UDFs414to execute nearest neighbor computations using the vectors402from the current word-embedding model. Thus, CI queries416provide approximate answers that reflect a given model.

Referring toFIG. 4, the relational tables408is output to tokenized relations406. The learned vectors402are generated from the external text sources404and the tokenized relations406. The learned vectors402is sent to the relational system tables. Additionally, the pre-computed external learned vectors410are also sent to the relational system tables412. The UDF uses and updates the relational system tables412.

The CI queries416can be broadly classified into four categories as follows: (1) Similarity/Dissimilarity Queries, (2) Inductive Reasoning Queries, (3) Cognitive OLAP Queries; and (4) Cognitive Extensions to the Relational DataModel.

In Similarity/Dissimilarity Queries, the basic UDF that compares two sets of relational variables can be integrated into an existing SQL query to form a similarity CI query. For example, a CI query can be:

In the CI similarity Query, the system finds similar entities to a given entity (VENDOR_NAME) based on transaction characteristic similarities.

The query can use a UDF, similarityUDF( ), that computes similarity score between two sets of vectors, that correspond to the items purchased by the corresponding customers. The purchased item list can be viewed as an unordered bag of items; and individual pairwise distances contribute equally to the final result. The query shown inFIG. 4uses the similarity score to select rows with identify similar customers based on their overall purchasing pattern as evidenced in a number of rows. The word embedding model creates a vector for each customer name that captures the overall purchases made by that customer.

Then, the customers with similar purchase patterns would have vectors that are close using the cosine distance metric. This query can be customized to restrict the purchases to a particular time period, e.g., a specific quarter or a month. The query would use vector additions over vectors to compute new vectors (e.g., create a vector for purchasing patterns of a customer custA in quarter Q3 by adding vectors for custA and quarter_Q3), and use the modified vectors to find the target customers. The patterns observed in these queries can be applied to other domains as well, e.g., identifying patients that are taking similar drugs, but with different brand names, or identifying food items with similar ingredients, or recommending mutual funds with similar investment strategies. The similarity query can be applied to other data types, such as images.

Solutions to inductive reasoning queries exploit latent semantic structure in the trained model via algebraic operations on the corresponding vectors. On can encapsulate these operations in UDFs to support following five types of inductive reasoning queries: analogies, semantic clustering, and analogy sequences, clustered analogies, and oddman-out.

In Inductive Reasoning Queries, a unique feature of word embedding vectors is their capability to answer inductive reasoning queries that enable an individual to reason from part to whole, or from particular to general.

A common way of expressing an analogy is to use relationship between a pair of entities, source_1 and target_1, to reason about a possible target entity, target_2, associated with another known source entity, source_2. An example of an analogy query is Lawyer:Client::Doctor:?, whose answer is Patient. To solve an analogy problem of the form X:Y::Q:?, one needs to find a token W whose meaning vector, VW, is closest to the ideal response vector VR, where VR=VQ+VY−VX.

Cognitive OLAP Queries can include a simple example of using semantic similarities in the context of a traditional SQL aggregation query. This CI query aims to extract the maximum sale amount for each product category in the sales table for each merchant that is similar to a specified merchant, Merchant_Y. The result is collated using the values of the product category. As illustrated earlier, the UDF similarity UDF can also be used for identifying customers that are different than the specified merchant. The UDF can use either an externally trained or locally trained model. This query can be easily adapted to sup- port other SQL aggregation functions such as MAX( ), MIN( ), and AVG( ). This query can be further extended to support ROLLUP operations over the aggregated values.

Cognitive Extensions to the Relational Data Model is as follows. There are powerful extensions to SQL that are enabled by word vectors. For this one needs the ability to refer to constituent tokens (extracted during textification) in columns of rows, in whole rows and in whole relations. The extension is via a declaration, in the FROM clause, of the form Token el that states that variable el refers to a token. To locate a token we use, in the WHERE clause, predicates of the form contains (E, el) where E can be a column in a row (e.g., EMP.Address), a whole row (e.g., EMP.*) or a whole relation (e.g., EMP). With this extension we can easily express queries such as asking for an employee whose Address contains a token which is very close to a token in a row in the DEPT relation (FIG. 12). Furthermore, we can also extend SQL with relational variables, say of the form $R and column variables, say X, whose names are not specified at query writing time; they are bound at runtime. We can then use these variables in queries, in conjunction with Token variables. This enables data-base querying without explicit schema knowledge which is useful for exploring a database. Interestingly, the notation $R.X is basically syntactic sugar. A software translation tool can substitute for $R.X an actual table name and an actual column. Then, perform the query for each such substitution and return the union of the results.

Cognitive queries in practice are as follows. The following illustrates some unique capabilities of the present cognitive database system by discussing a scenario in which CI queries are used to gain novel insights from a multi-modal relational data base. In this scenario, one considers a database of national parks across multiple countries, with links to images of animals in the associated national parks. The system uses images from the open source Image database, ImageNet, to populate our database. The system can use this database to present results from inductive reasoning CI queries.

Although the database under evaluation is fairly simple, its architecture is similar to many other real-life databases, e.g., a multi-modal patient database with text fields describing patient characteristics and image fields referring to associated images (e.g., radiology or FMRI images), or an insurance claims database with text fields containing the claim information and image fields storing supporting pictures (e.g., car collision photos).

FIG. 5illustrates training from source database in an exemplary embodiment. Relational tables502are then inputted for data cleaning504, which then is sent as text to create unique tokens516, the numerical values can use k-means clustering406, while images can get image tags520. Cluster the vectors of these relevant tokens into k clusters, say using k-means. The clustering can include creating a two columns table TT recording in column ClusterNum the cluster number of each relevant token's vector. The tokens are in column Token. From the clustering at506, there is then created unique tokens508.

The training text file510is performed from the unique tokens for the text516, the tokens for the numerical values and image features522generated from the images. The training text file510is then inputted for word embedding training512. The word embedding training512can be tuned for windows size with vector dimensions518. The word embedding training512then generates the word embedding model514.

FIG. 6illustrates a cognitive database with an example execution flow in an exemplary embodiment. For example,FIG. 6can be a SPARK execution flow, or any other mode of distributing computations over a cluster of machines using a distributed infrastructure. The input table602can include the source data610to output to dataframe612and specialized word embedding614during the trained model phase604. The specialized word embedding614outputs dataframe616, which is then input to SQL618for SQL Query phase606. Similarity computation640can be made as a SQL UDF nearest neighbour620. The SQL618query then outputs to the output table as a dataframe622.

FIG. 7illustrates an application database with links to images in an exemplary embodiment. The database702shows the picture IDs with the links to the pictures. The Internal Training database is then shown with features extracted from linked images in704such as the color, dietary habit, class, etc. The merged data704is used as an input to train the word embedding model that generates embeddings of each unique token based on the neighborhood. Each row of the database is viewed as a sentence.

FIG. 8illustrates an example application in an exemplary embodiment. CI Query using external knowledge base: Find all images of animals whose classD similarity score to the Concept of “Hypercarnivore” of WIKIPEDIA using proximityAvgForExtKB UDF is greater than 0.5. Exclude images that are already tagged as carnivore, herbivore, omnivore or scavenger.

SELECT X.imagename,X.classA,X.classB,X.classC, X.classD FROM ImageDataTable X

WHERE

ORDER BY SimScore DESC

The Example inFIG. 8demonstrates the use of an external semantic model for querying a multi-modal database. In this scenario, the system first trains a word embedding model from an external knowledge base derived from an internet definition (e.g., WIKIPEDIA, etc.). Similar to the model trained from the database, the external model assigns d dimensional meaning vectors to unique tokens (for the external model, we use d=200). From the definition model, we select a token associated with a concept Hypercarnivore, which refers to a class of animals whose diet has more than 70% meat. Examples of hyper-carnivores include lions, sharks, polar bears, crocodiles, hyenas, etc. Therefore, in our model, the Hypercarnivore meaning vector is related to meaning vectors of tokens shark, crocodiles, etc. For this query, the system employs this externally trained model to extract images that are similar to the concept Hypercarnivore. The UDF proximityAvgForExtKB( )uses the external model, finds images from the database whose classD features (i.e., names) are related to Hypercarnivore, and returns those images whose similarity score is higher than 0.5.FIG. 8shows the CI query and its result: pictures of hyenas802-808in display800, who are members of the hypercarnivore class3. This example also demonstrates the unique capability of cognitive databases that allows querying a database using a token not present in the database. In this case, both the original and training databases do not contain the token Hypercarnivore.

FIG. 8is only an example, as CI queries are applicable to a broad class of domains. These include finance, insurance, retail, customer care, log analytics, healthcare, genomics, semantic search over documents (patent, legal, or financial), healthcare informatics, and human resource management.

Therefore, it is shown a novel relational database system that uses word embedding approach to enable semantic queries in SQL. Also shown is Spark-based implementation that loads data from a variety of sources and invokes Cognitive Intelligence queries using Spark SQL. There was shown a demonstration of the cognitive database capabilities using a multi-modal (text+image) dataset. In addition, there was an illustration of seamlessly integrating AI capabilities into relational database ecosystem.

FIG. 9illustrates another hardware configuration of an information handling/computer system1100in accordance with the present invention and which preferably has at least one processor or central processing unit (CPU)1110that can implement the techniques of the invention in a form of a software program.

The CPUs1110are interconnected via a system bus1112to a random access memory (RAM)1114, read-only memory (ROM)1116, input/output (I/O) adapter1118(for connecting peripheral devices such as disk units1121and tape drives1140to the bus1112), user interface adapter1122(for connecting a keyboard1124, mouse1126, speaker1128, microphone1132, and/or other user interface device to the bus1112), a communication adapter1134for connecting an information handling system to a data processing network, the Internet, an Intranet, a personal area network (PAN), etc., and a display adapter1136for connecting the bus1112to a display device1138and/or printer1139(e.g., a digital printer or the like).

In addition to the hardware/software environment described above, a different aspect of the invention includes a computer-implemented method for performing the above method. As an example, this method may be implemented in the particular environment discussed above.

Thus, this aspect of the present invention is directed to a programmed product, including signal-bearing storage media tangibly embodying a program of machine-readable instructions executable by a digital data processor incorporating the CPU1110and hardware above, to perform the method of the invention.

This signal-bearing storage media may include, for example, a RAM contained within the CPU1110, as represented by the fast-access storage for example.

Alternatively, the instructions may be contained in another signal-bearing storage media1200, such as a magnetic data storage diskette1210or optical storage diskette1220(FIG. 10), directly or indirectly accessible by the CPU1210.

Whether contained in the diskette1210, the optical disk1220, the computer/CPU1210, or elsewhere, the instructions may be stored on a variety of machine-readable data storage media.

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 includes an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

Referring now toFIG. 11, a schematic1400of an example of a cloud computing node is shown. Cloud computing node1400is only one example of a suitable cloud computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the invention described herein. Regardless, cloud computing node1400is capable of being implemented and/or performing any of the functionality set forth hereinabove.

As shown inFIG. 11, computer system/server1412in cloud computing node1400is shown in the form of a general-purpose computing device. The components of computer system/server1412may include, but are not limited to, one or more processors or processing units1416, a system memory1428, and a bus1418that couples various system components including system memory1428to processor1416.

Computer system/server1412typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server1412, and it includes both volatile and non-volatile media, removable and non-removable media.

System memory1428can include computer system readable media in the form of volatile memory, such as random-access memory (RAM)1430and/or cache memory1432. Computer system/server1412may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system1434can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus1418by one or more data media interfaces. As will be further depicted and described below, memory1428may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program/utility1440, having a set (at least one) of program modules1442, may be stored in memory1428by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules1442generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

Computer system/server1412may also communicate with one or more external devices1414such as a keyboard, a pointing device, a display1424, etc.; one or more devices that enable a user to interact with computer system/server1412; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server1412to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces1422. Still yet, computer system/server1412can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter1420. As depicted, network adapter1420communicates with the other components of computer system/server1412via bus1418. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server1412. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Referring now toFIG. 12, illustrative cloud computing environment1550is depicted. As shown, cloud computing environment1550includes one or more cloud computing nodes1400with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone1554A, desktop computer1554B, laptop computer1554C, and/or automobile computer system1554N may communicate. Nodes1400may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment1550to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices1554A-N shown inFIG. 12are intended to be illustrative only and that computing nodes1400and cloud computing environment1550can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Virtualization layer1662provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients.

Workloads layer1666provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include such functions as mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; transaction processing; and, more particularly relative to the present invention, the APIs and run-time system components of generating search autocomplete suggestions based on contextual input.