RECORDS PROCESSING BASED ON RECORD ATTRIBUTE EMBEDDINGS

One or more trained embedding generation artificial intelligence models are executed to generate a plurality of record attribute embeddings. The plurality of record attribute embeddings represents a plurality of attributes of data of a plurality of records. Grouping of the plurality of record attribute embeddings is performed. The grouping of a record attribute embedding includes grouping attribute values of the record attribute embedding into one or more groups of attribute values. The performing grouping provides a plurality of groups of attribute values for the plurality of record attribute embeddings. Selected records are compared to provide a set of matched records. The comparing, based on a group of attribute values, includes comparing records that include one or more attribute values grouped in the group of attribute values providing a subset of matched records of the set of matched records. The set of matched records is stored in an accessible computer location.

BACKGROUND

One or more aspects relate, in general, to facilitating processing within a computing environment, and in particular, to facilitating the processing of data within the computing environment.

Certain aspects relating to the processing of data include matching data records. For instance, to match a pair of records, extensive comparison of different record attributes, such as name, address, date of birth, identifier, is performed. Due to an exponential number of comparisons to compare each record to one another, a bucketing-based candidate selection technique is used.

The bucketing-based candidate selection technique is based on equality of a hash value of features of the records. The hash-based bucketing technique enables the comparison of similar records while reducing the total number of comparisons. With a hash-based candidate selection, a record is included in the bucket if an attribute value of the record produces the exact same hash value.

SUMMARY

Shortcomings of the prior art are overcome, and additional advantages are provided through the provision of a computer-implemented method of facilitating processing within a computing environment. The computer-implemented method includes obtaining, by at least one computing device of the computing environment, data of a plurality of records. The at least one computing device of the computing environment executes one or more trained embedding generation artificial intelligence models to generate a plurality of record attribute embeddings based on the data. The plurality of record attribute embeddings represents a plurality of attributes of the data. Grouping is performed, using the at least one computing device of the computing environment, of the plurality of record attribute embeddings. The performing grouping of a record attribute embedding of the plurality of record attribute embeddings includes grouping attribute values of the record attribute embedding into one or more groups of attribute values based on one or more selected criteria. The performing grouping provides a plurality of groups of attribute values for the plurality of record attribute embeddings. Using the at least one computing device of the computing environment and based on the plurality of groups of attribute values, selected records of the plurality of records are compared to provide a set of matched records. The comparing, based on a group of attribute values of the plurality of groups of attribute values, includes comparing records that include one or more attribute values grouped in the group of attribute values providing a subset of matched records of the set of matched records. Using the at least one computing device of the computing environment, the set of matched records is stored in a computer location accessible by one or more users of the set of matched records.

Computer systems and computer program products relating to one or more aspects are also described and may be claimed herein. Further, services relating to one or more aspects are also described and may be claimed herein.

DETAILED DESCRIPTION

In one or more aspects, a capability is provided to facilitate processing within a computing environment. In one or more aspects, a capability is provided to facilitate data processing in which data accessibility is improved and storage (e.g., memory, storage, and/or a combination of memory/storage) requirements are reduced. Processing within the computing environment is enhanced by improving the processing of data records, including, but not limited to, candidate selection of records to be compared, such that the processing is streamlined, storage requirements are reduced, and memory access is improved.

One or more aspects of the present disclosure are incorporated in, performed and/or used by a computing environment. As examples, the computing environment may be of various architectures and of various types, including, but not limited to: personal computing, client-server, distributed, virtual, emulated, partitioned, non-partitioned, cloud-based, quantum, grid, time-sharing, cluster, peer-to-peer, wearable, mobile, having one node or multiple nodes, having one processor or multiple processors, and/or any other type of environment and/or configuration, etc. that is capable of executing a process (or multiple processes) that, e.g., performs records processing and/or performs one or more other aspects of the present disclosure. Aspects of the present disclosure are not limited to a particular architecture or environment.

One example of a computing environment to perform, incorporate and/or use one or more aspects of the present disclosure is described with reference toFIG.1. In one example, a computing environment100contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as records processing code or module150. In addition to block150, computing environment100includes, for example, computer101, wide area network (WAN)102, end user device (EUD)103, remote server104, public cloud105, and private cloud106. In this embodiment, computer101includes processor set110(including processing circuitry120and cache121), communication fabric111, volatile memory112, persistent storage113(including operating system122and block150, as identified above), peripheral device set114(including user interface (UI) device set123, storage124, and Internet of Things (IoT) sensor set125), and network module115. Remote server104includes remote database130. Public cloud105includes gateway140, cloud orchestration module141, host physical machine set142, virtual machine set143, and container set144.

The computing environment described above is only one example of a computing environment to incorporate, perform and/or use one or more aspects of the present disclosure. Other examples are possible. For instance, in one or more embodiments, one or more of the components/modules ofFIG.1are not included in the computing environment and/or are not used for one or more aspects of the present disclosure. Further, in one or more embodiments, additional and/or other components/modules may be used. Other variations are possible.

In one example, to process data records, a records processing module (e.g., records processing module150) is used, in accordance with one or more aspects of the present disclosure. A records processing module (e.g., records processing module150) includes code or instructions used to process records, including performing candidate selection of records to be compared, in accordance with one or more aspects of the present disclosure. A records processing module (e.g., records processing module150) includes, in one example, various sub-modules to be used to perform the records processing. The sub-modules are, e.g., computer readable program code (e.g., instructions) in computer readable media, e.g., storage (persistent storage113, storage124, cache121, other storage, as examples). The computer readable media may be part of a computer program product and the computer readable program code may be executed by and/or using one or more computing devices (e.g., one or more computers, such as computer(s)101; one or more end user devices, such as end user device(s)103; one or more servers, such as server(s)104; one or more processors, such as processor(s) of processor set110; processing circuitry, such as processing circuitry of processor set110; and/or other computing devices, etc.). Additional and/or other computers, servers, devices, processors, processing circuitry and/or other computing devices may be used to execute one or more of the sub-modules and/or portions thereof. Many examples are possible.

One example of records processing module150is described with reference toFIG.2. In one example, records processing module150includes a records data standardization sub-module200used to standardize data of the records to be processed; an attributes embedding generation sub-module210used to generate attributes embeddings of the data (e.g., standardized data); a group embeddings sub-module220to cluster attribute values of each of the embeddings into one or more groups based on, e.g., similarity of the attribute values, as defined by one or more selected criteria (e.g., a cosine similarity or other similarity-identifying technique); a compare records sub-module230to compare records based on the groups provided by group embeddings sub-module220; and a store set of matched records sub-module240to store the set of matched records in a computer location (e.g., memory, storage, etc.) for access by one or more users of the records. Although various sub-modules are described, a records processing module, such as records processing module150, may include additional, fewer and/or different sub-modules. A particular sub-module may include additional code, including code of other sub-modules, less code, and/or different code. Further, additional and/or other modules may be used to process records. Many variations are possible.

The sub-modules are used, in accordance with one or more aspects of the present disclosure, to perform records processing, as further described with reference toFIG.3. In one example, a records processing process300is executed by one or more computing devices (e.g., one or more computers (e.g., computer101, other computer(s), etc.), one or more end user devices (e.g., end user device103, other device(s)), one or more servers (e.g., server104, other server(s)), one or more processors and/or processing circuitry (e.g., of processor set110or other processor sets), and/or one or more other computing devices, etc.). Although example computing devices, computers, servers, devices, processors and/or processing circuitry are provided, additional, fewer and/or other computers, servers, devices, processors, processing circuitry and/or computing devices may be used for the records processing process. Various options are possible.

Referring toFIG.3, in one example, a records processing process300performs310a standardization process to standardize data of a plurality of records to be processed, providing standardized data. For instance, a record may include one or more attributes of the data, such as name, address, date of birth, identifier, other attributes, a combination of one or more attributes, etc., and values of those attributes are standardized. This includes, for instance, using a standard transformation that, as examples, standardizes alphabetic characters, removes punctuation, checks for anonymous values and may ignore or eliminate those, standardizes words or phrases, etc. As an example, if one record has an address attribute and the value of the address attribute has a street name that includes “Street” and another record has a street name that includes “St.”, then the standardizing technique would standardize them to the same value, such as “Street” or another standardizing value. Other examples and variations are possible. The data (e.g., attribute values) to be standardized may be all of the data of the plurality of records or selected data of the plurality of records. The data selected to be standardized is, in one example, the one or more attributes of the data to be used in further processing herein. Other examples are also possible.

Using the standardized data (e.g., standardized attribute values), process300generates320attribute embeddings. For example, process300uses one or more trained embedding generation artificial intelligence models to generate a plurality of record attribute embeddings based on the standardized data. The plurality of record attribute embeddings represents attributes of the data of the plurality of records. For instance, if one or more of the records being processed includes attributes of name, address, date of birth, identifier, as examples (additional, fewer and/or other attributes may be included within one or more of the records being processed), then process300may execute the one or more trained embedding generation artificial intelligence models to generate a record attributes embedding for name, another for address, another for date of birth, and yet another for identifier, etc. Additional, fewer and/or other record attribute embeddings may be generated for the record attributes using the one or more trained embedding generation artificial intelligence models.

In one example, each record attribute embedding is output as a multi-dimensional vector that represents the attributes with their context. In one example, the embedding generation models are used to generate the attribute embeddings in a Euclidean space—a finite-dimensional inner product space over real numbers—(e.g., a very high dimensional Euclidean space).

In one or more aspects, initially, the embedding generation model(s) used to generate the embeddings are trained with data other than the data being processed herein. For instance, they are trained with natural language models and processing that uses generic data (i.e., data that is not necessarily of the records being processed). However, in one or more aspects, the embedding generation model(s) used may be further trained using selected data of the data being processed to enhance the models and increase accuracy of the output of the models. This further training may be performed at selected times, such as at defined intervals, repeatedly, etc. In each iteration, the model learns based on the new training input. One or more variations are possible.

Based on the multi-dimensional embeddings of record attribute values, process300performs grouping330of the attribute embeddings to optimize candidate selection of records for comparison. In one example, the grouping is performed for each record attribute embedding providing one or more groups of attribute values for each record attribute embedding. The grouping of a record attribute embedding into one or more groups of attribute values is based on one or more selected criteria (e.g., similarity of attributes as defined by one or more criteria, such as a cosine similarity or other techniques used to determine similarity). Each group of attribute values for a record attribute embedding includes attribute values of one or more records of the plurality of records being processed. For instance, if a record attribute embedding is for the attribute referred to as address, and a group of attribute values for the record attribute embedding includes an address value of Street A, then one or more records of the plurality of records include that attribute value and correspond to that group of attribute values.

In one example, process300uses an unsupervised clustering technique to perform the grouping for each of the record attribute embeddings. The unsupervised clustering technique clusters a record attribute embedding into one or more groups based on similarity, producing groups (also referred to as buckets or clusters) that represent records based on attribute value similarities. In one or more examples, the one or more selected criteria is a cosine similarity criterion. Using, for instance, cosine similarity, each attribute value of the record attribute embedding (which in one example is represented as a multi-dimensional vector) is placed in a group. For instance, the cosine angle between two vectors is determined and that value is compared to a selected threshold. If the value is 1 or close to one (e.g., within a selected range, such as 0.7-1.0, or other selected ranges), then the attribute value is placed in a group of similar attribute values. Other examples and/or techniques are possible.

Each group, in one example, is treated as one bucket or cluster of attributes used to provide candidate records for comparison. For instance, process300compares340the records corresponding to a group to determine if there are any matches. For instance, process300performs pairwise comparison of records identified from the group. If they are a match, as defined by a selected threshold (e.g., above a threshold, below a threshold, within a range of the threshold, etc.), the pair is considered a match and placed in a subset of matched records, which may also be referred to as an entity.

In one example, a plurality of subsets of matched records is determined based on the groups defined for the plurality of record attribute embeddings. The plurality of subsets of matched records are consolidated, in one example, into a set of matched records. Process300stores350the set of matched records in a computer location (e.g., storage, memory) accessible to one or more users of the records. A record of the matched set of records may represent one or more other records that are different but found to be similar based on one or more selected criteria.

In one example, a user may perform a search of the stored set of matched records to obtain information to be used in many technical or technological areas, including, but not limited to, computer processing, manufacturing and/or other industries providing products and/or services. The user may, for instance, search the stored set of matched records to obtain parts information on one or more parts to be used in building or upgrading a computer or in manufacturing a component, as examples. Many uses are possible.

In one example, the searching is performed automatically using one or more computing devices and the obtained information or data may be used in a process (e.g., an automatic process) to build, upgrade and/or manufacture, as just some examples. Again, many possibilities exist. One or more of the improvements in record processing provided in accordance with one or more aspects of the present disclosure provides improvements in one or more technical or technological areas, including, but not limited to, those provided herein.

In one or more aspects, records are grouped based on the similarity of attribute values which enhances the quality of candidate selection for the comparison of records, achieving an optimized candidate selection. In one or more aspects, records data are standardized using standardizing transformations. Embedding generation models are utilized to generate attribute embeddings in, e.g., a very high dimensional Euclidean space. In one aspect, the embeddings are, e.g., multi-dimensional vectors that represent the attributes with their context. In one aspect, an unsupervised clustering technique is used to group these multi-dimensional embeddings into groups based on similarity, producing buckets that contain records based on attribute value similarities. Each cluster is treated as one bucket and is used for the candidate selection of records for comparison.

As described herein, in one or more aspects, computer processing is improved by grouping records based on similarity of attribute values, which enhances the quality of candidate selection for the comparison of records, achieving optimized candidate selection for comparison. In one or more aspects, data storage and access within a computing environment are enhanced by receiving data from multiple sources, processing the data received from the multiple sources to provide a matched set of records and storing the matched set of records in a computer location accessible to one or more users. The matched set of records is accessible to the one or more users via, e.g., one or more computer networks facilitating access/use of the data.

In one or more aspects, use of storage is reduced by generating the matched set of records that consolidates multiple similar but different records into one record, eliminating the need to store the similar but different records in the matched set of records. Processing within the computer environment is improved by reducing the number of records that are to be searched when looking for a particular record. For instance, processing time in searching, storing, maintaining and/or updating the matched set of records is improved. Thus, the functioning of a computer is improved, in one or more aspects, by, for instance, reducing the amount of storage needed to store the matched set of records since multiple records are consolidated into one record, and reducing processing time in searching and accessing the records since the multiple records are consolidated.

Described above is one example of records processing. One or more aspects of the process may use machine learning. For instance, machine learning may be used to learn of data access, data usage (e.g., access frequencies, access patterns), data similarities, query patterns, search terms distribution, to predict access usage, to predict query patterns, perform analysis and/or perform other tasks. A system is trained to perform analyses and learn from input data and/or choices made.

FIG.4is one example of a machine learning training system400that may be utilized, in one or more aspects, to perform cognitive analyses of various inputs, including input data, data from one or more data structures and/or other data. Training data utilized to train the model in one or more embodiments of the present disclosure includes, for instance, data that pertains to one or more events, such as natural language processing data, record data being processed, etc. The program code in embodiments of the present disclosure performs a cognitive analysis to generate one or more training data structures, including algorithms utilized by the program code to predict states of a given event. Machine learning (ML) solves problems that are not solved with numerical means alone. In this ML-based example, program code extracts various attributes from ML training data410(e.g., historical attribute data collected from various data sources relevant to the event), which may be resident in one or more databases420comprising event or task-related data and general data. Attributes415are utilized to develop a predictor function, h (x), also referred to as a hypothesis, which the program code utilizes as a machine learning model430.

In identifying various event states, features, attribute similarities, constraints and/or behaviors indicative of states in the ML training data410, the program code can utilize various techniques to identify attributes in an embodiment of the present disclosure. Embodiments of the present disclosure utilize varying techniques to select attributes (data attributes, elements, patterns, features, constraints, distribution, etc.), including but not limited to, diffusion mapping, principal component analysis, recursive feature elimination (a brute force approach to selecting attributes), and/or a Random Forest, to select the attributes related to various events. The program code may utilize a machine learning algorithm440to train the machine learning model430(e.g., the algorithms utilized by the program code), including providing weights for the conclusions, so that the program code can train the predictor functions that comprise the machine learning model430. The conclusions may be evaluated by a quality metric450. By selecting a diverse set of ML training data410, the program code trains the machine learning model430to identify and weight various attributes (e.g., data attributes, features, patterns, constraints, distributions, etc.) that correlate to various states of an event.

The model generated by the program code is self-learning as the program code updates the model based on active event feedback, as well as from the feedback received from data related to the event. For example, when the program code determines that there is a constraint, event, similarity or pattern (e.g., data attribute, record attribute similarity, query pattern, data distribution, search terms distribution, etc.) that was not previously predicted by the model, the program code utilizes a learning agent to update the model to reflect the state of the event, in order to improve predictions in the future. Additionally, when the program code determines that a prediction is incorrect, either based on receiving user feedback through an interface or based on monitoring related to the event, the program code updates the model to reflect the inaccuracy of the prediction for the given period of time. Program code comprising a learning agent cognitively analyzes the data deviating from the modeled expectations and adjusts the model to increase the accuracy of the model, moving forward.

In one or more embodiments, program code, executing on one or more processors, utilizes an existing cognitive analysis tool or agent (now known or later developed) to tune the model, based on data obtained from one or more data sources. In one or more embodiments, the program code interfaces with application programming interfaces to perform a cognitive analysis of obtained data. Specifically, in one or more embodiments, certain application programming interfaces comprise a cognitive agent (e.g., learning agent) that includes one or more programs, including, but not limited to, natural language classifiers, a retrieve and rank service that can surface the most relevant information from a collection of documents, concepts/visual insights, trade off analytics, document conversion, and/or relationship extraction. In an embodiment, one or more programs analyze the data obtained by the program code across various sources utilizing one or more of a natural language classifier, retrieve and rank application programming interfaces, and trade off analytics application programming interfaces. An application programming interface can also provide audio related application programming interface services, in the event that the collected data includes audio, which can be utilized by the program code, including but not limited to natural language processing, text to speech capabilities, and/or translation.

In one or more embodiments, the program code utilizes a neural network to analyze event-related data to generate the model utilized to predict the state of a given event at a given time. Neural networks are a biologically-inspired programming paradigm which enable a computer to learn and solve artificial intelligence problems. This learning is referred to as deep learning, which is a subset of machine learning, an aspect of artificial intelligence, and includes a set of techniques for learning in neural networks. Neural networks, including modular neural networks, are capable of pattern recognition with speed, accuracy, and efficiency, in situations where data sets are multiple and expansive, including across a distributed network, including but not limited to, cloud computing systems. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to identify patterns (or similarities) in data (i.e., neural networks are non-linear statistical data modeling or decision making tools). In general, program code utilizing neural networks can model complex relationships between inputs and outputs and identify patterns in data. Because of the speed and efficiency of neural networks, especially when parsing multiple complex data sets, neural networks and deep learning provide solutions to many problems in multiple source processing, which the program code in one or more embodiments accomplishes when obtaining data and generating a model for predicting states of a given event.

One or more aspects of the present disclosure are tied to computer technology and facilitate processing within a computer, improving performance thereof. For instance, storage requirements and costs are reduced, along with processing time and resources to use the matched set of records. Response time for queries may be improved. Processing within a processor, computer system and/or computing environment is improved.

One or more aspects optimize records processing by enhancing the candidate selection process in which records are grouped together that have similar attribute values and not just the exact same values. This increases the number of records to be compared and further reduces the number of records to be accessed or searched after the comparing process is complete, since the matched set of records consolidates more records than comparisons based on other techniques, such as hash-value based candidate selection.

The computing environments described herein are only examples of computing environments that can be used. One or more aspects of the present disclosure may be used with many types of environments. The computing environments provided herein are only examples. Each computing environment is capable of being configured to include one or more aspects of the present disclosure. For instance, each may be configured to perform records processing and/or to perform one or more other aspects of the present disclosure.

Although various embodiments are described above, these are only examples. Other types of embedding generation models and/or clustering techniques may be used. Many variations are possible.

Various aspects and embodiments are described herein. Further, many variations are possible without departing from a spirit of aspects of the present disclosure. It should be noted that, unless otherwise inconsistent, each aspect or feature described and/or claimed herein, and variants thereof, may be combinable with any other aspect or feature.