International Business Machines Corporation

Informix Software, Inc.

Paul Sherwood Taylor

Roger C Raphael

Harish Deshmukh

Rajesh M Desai

Philip E Parker

Gabriel Valencia

Bruce Gilbert Lindsay

Sridhar Rajagopalan

Scott David Lashley

Robert H Gerber

Alon Shalev Housfater

Chitrang Shah

Chen Wang

Ju Wei Shi

Xiao Yang

James Allen Reimer

Yun Jie Qiu

Michael G Winter

Li Mei Jiao

Edward T Morgan

Frank C Hayes

Ning Li

Manish Anand Bhide

Ronald Jason Barber

Sameep Mehta

Cheng-Ta Lee

Eugene Jon Shekita

Ronald B Williams

Jian Lin

Ronen Grosman

Gary Valentin

Kevin Leo Beck

Matthew Elsner

Murali M Krishna

Michael Josiah Bolding

Paul Michael Brett

Anurag Kashyap

Yun Pan

Prakash Sundaresan

Samiulla Shaikh

Shabharesh Gudla

Prishni Rateria

Shengsong Ni

Scott Alan Shurts

Neelesh K Shukla

A tiered machine learning-based infrastructure comprises a first machine learning (ML) tier configured to execute within an enterprise network environment and that learns statistics for a set of use cases locally, and to alert deviations from the learned distributions. Use cases typically are independent from one another. A second machine learning tier executes external to the enterprise network environment and provides further learning support, e.g., by determining a correlation among multiple independent use cases that are running locally in the first tier. Preferably, the second tier executes in a cloud compute environment for scalability and performance.

Distributed machine learning for anomaly detection

Technical solutions are described to implement a scalable write ahead log using a distributed file system. A general aspect includes a method for providing consistency among metadata replicas and content in an enterprise content management cluster. The method includes recording a transaction log entry in response to receiving a content modification request, the transaction log entry including a version identifier set to a first version value. The method also includes updating the transaction log entry to a second version value in response to successfully modifying content and one of a plurality of metadata replicas containing metadata corresponding to the request. The method also includes updating the transaction log entry to a third version value in response to successfully modifying each of the metadata replicas. The present document further describes examples of other aspects such as methods, computer products.

Scalable enterprise content management

Provided are a computer program product, system, and method for distributed processing of a query with distributed posting lists. A dispatch map has entries, wherein each entry identifies one of a plurality of terms in a dictionary, wherein for each of the terms there is a posting list identifying zero or more objects including the term, wherein at least one of the dispatch map entries indicate at least one distributed processing element including the posting list for the term. The dispatch map is used to dispatch sub-expressions comprising portions of a query to distributed processing elements having the posting lists for terms in the sub-expressions, wherein the distributed processing elements distributed the sub-expressions execute the sub-expressions on the posting lists for the terms in the sub-expression.

Distributing posting lists to processing elements

A data structure is generated containing enumerators for data types of a domain, text forms of the enumerators and context patterns for the text forms. The data structure also includes information extraction rules that are associated with the enumerators. The data structure is updated with additional context patterns and text forms that are identified within a set of documents to which text analytic annotators are to be tuned. The set of documents are analyzed against the updated data structure and additional extraction rules are generated based on the analysis.

Systematic tuning of text analytic annotators

A method includes determining, by a controller, a portion of data that is selected by a user. The portion of data includes source data that is to be transformed by at least one shaping function. The method also includes generating, by the controller, a first output recommendation data that communicates at least one recommended shaping function to apply to the portion of data. The first output recommendation data is generated based on patterns of shaping functions that have been previously chosen. The patterns of shaping functions that have been previously chosen can be chosen by a plurality of system users. The method also includes determining whether to apply the at least one recommended shaping function to the portion of data. The method also includes applying the at least one recommended shaping function based on the determining.

Generating a recommended shaping function to integrate data within a data repository

Distributed processing of a search query with distributed posting lists

Systematic tuning of text analytic annotators with specialized information

In an approach for parallelizing document processing in an information handling system, a processor receives a document, wherein the document includes text content. A processor extracts information from the text content, utilizing natural language processing and semantic analysis, to form tokenized semantic partitions, comprising a plurality of sub-documents. A processor schedules a plurality of concurrently executing threads to process the plurality of sub-documents.

Parallelizing semantically split documents for processing

Mechanisms are provided, in a cloud computing data processing system comprising a plurality of components, hosted on one or more hardware computing devices of the cloud computing data processing system, for transactional resource and topology management. A cloud service system is instantiated that comprises component instances having allocated resources. The cloud service system comprises orchestration engine logic which generates a transactional data structure that stores objects corresponding to transactions executed in association with resources and component instance state changes. The orchestration engine logic receives transactions associated with resources or component instances of the cloud service system and records the transactions as objects in the transactional data structure in response to receiving the transactions. In response to detecting an exception during operation of the cloud service system, a recovery engine utilizes the objects in the transactional data structure to restart or recover the cloud service system.

Transactional orchestration of resource management and system topology in a cloud environment

A method and system for distributing tasks from an external application among concurrent database application server instances in a database system for optimum load balancing, based on consensus among the instances. Each application instance identifies a task partition ownership by those in a membership group based on a time window and generates a new membership group and partition ownership based on the current partition ownership. The instance makes the new membership group and partition ownership known to other members by recoding them in the membership table and partition map. Each participation by an instance in the membership group is identified by a random number. The new membership group and partition ownership are generated and adjusted based on an average partition allocation to achieve consensus among the instances.

Method and system for distributed task dispatch in a multi-application environment based on consensus

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Redwood City, CA, United States of America

Paul Sherwood Taylor