International Business Machines Corporation

Globalfoundries Inc.

John Sismanis

Peter Haas

Andrey Balmin

Berthold Reinwald

Yuanyuan Tian

Rainer Gemulla

Vuk Ercegovac

Bin Bi

Liping Peng

Wenlei Xie

Kevin Scott Beyer

Rajesh Manjrekar

Akanksha Baid

Alkis Simitsis

Wensheng Wu

A computer-implemented method, according to one embodiment, includes: generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph, generating two or more partial results by executing an algorithm on the two or more sample graphs, combining the partial results into a final result, and incrementally maintaining the sample graphs. Edges included in the current snapshot of a dynamic graph and which were added to the dynamic graph in a most recent update thereto are included in each of the generated two or more sample graphs. Moreover, incrementally maintaining the sample graphs includes: subsampling each of the edges of each of the sample graphs at a given time by applying a Bernoulli trial, and combining a result of the subsampling with new edges received in a batch corresponding to the given time to form new sample graphs.

Dynamic interaction graphs with probabilistic edge decay

In one general embodiment, a computer-implemented method is provided for analyzing a dynamic graph. The computer-implemented method includes generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph. Additionally, the computer-implemented method includes generating two or more partial results by executing an algorithm on the sample graphs. Still yet, the computer-implemented method includes combining the partial results, from executing the algorithm on the sample graphs, into a final result.

A computer determines social media influencers in a specific topic by receiving a dataset of information associated with a website, the information including a first list of users of the website and a list of content that each user posts on the website, wherein each user is associated with other users from the first list of users. The computer determines initial values representing variables of the dataset of information on the website, wherein the variables include one or more topics for the list of content that each user from the first list of users posts on the website. The computer performs an iteration of Gibbs Sampling utilizing the initial values. The computer determines the one or more new values representing variables of the dataset represent a distribution of the one or more topics for the list of content that each user from the first list of users posts.

Identifying influencers for topics in social media

Stratified sampling of a plurality of records is performed. A plurality of records are partitioned into a plurality of splits, wherein each split includes at least a portion of the plurality of records. The split of the plurality of splits is provided to a mapper. The mapper assigns at least a portion the records of the at least one split to a group based on a strata of the assigned records, and filters the records of the group based on a comparison of the weights of the records to a local threshold of the mapper. The mapper updates the local threshold of the mapper by communicating with a coordinator. The mapper shuffles the group to a reducer, where the reducer filters the records of the group based on the weights of the records. The reducer provides a stratified sampling of the plurality of records based on the group.

Stratified sampling using adaptive parallel data processing

A computer-implemented method includes partitioning a plurality of records into a plurality of splits. Each split includes at least a portion of the plurality of records. The method further includes providing at least one split of the plurality of splits to a mapper. The mapper scans the input data set, transforms each input record using a map function, and extracts a grouping key in parallel. The method further includes assigning at least a portion the records of the at least one split to a group. Each assignment to the group is based on a strata of the assigned record, and filtering the records of the group. Each filtering is based on a comparison of a weight of a record to a local threshold of the mapper. The method further includes shuffling the group to a reducer and providing a stratified sampling of the plurality of records based on the group.

A computer determines social media influencers in a specific topic. The computer receives a dataset of information on a website, the information including a list of users of the website and a list of content that each user posts, wherein each user is associated with one or more other users. The computer identifies a plurality of variables associated with the dataset, wherein the plurality of variables represent the information of the dataset on the website. The computer executes a topic specific search based on the plurality of variables, the topic search providing at least another list of users representing influencers in a specific topic.

A system for performing time conversions that includes a processor configured to generate a set of target time-series data from a set of source time-series data and a memory containing specification information relating to the set of target time-series data and also containing information relating to the set of source time-series data. The source time-series specification and the target time-series specification include time intervals between data values. The system also includes a time alignment algorithm used by the processor for converting the set of source time-series data to the set of target time-series data. The converting includes calculation of a set of cubic-spline interpolation constants and the cubic-spline constants.

Interpolation techniques used for time alignment of multiple simulation models

Embodiments include a method, system, and computer program product for performing time alignments. The method includes receiving a specification request for generating a set of target time-series data from a set of source time-series data and obtaining specification information relating to the set of target time-series data and relating to the set of source time-series data. The specification also includes time intervals between data values. The method also includes converting the set of source time-series data to the set of target time-series data, wherein said converting includes calculating a set of cubic-spline interpolation constants.

Systems and methods directed toward processing optimization problems using loss functions, wherein a loss function is decomposed into at least one stratum loss function, a loss is decreased for each stratum loss function to a predefined stratum loss threshold individually using gradient descent, and the overall loss is decreased to a predefined threshold for the loss function by appropriately ordering the processing of the strata and spending appropriate processing time in each stratum. Other embodiments and aspects are also described herein.

Systems and methods for large-scale randomized optimization for problems with decomposable loss functions

A method and system for discovering keys in a database. A minimal set of non-keys of the database are found. The database includes at least two entities and at least two attributes. The minimal set of non-keys includes at least two non-keys. Each entity independently includes a value of each attribute. A set of keys of the database is generated from the minimal set of non-keys. Each key of the generated set of keys independently is a unitary key consisting of one attribute or a composite key consisting of at least two attributes.

Efficient discovery of keys in a database

A computer-implemented method for accessing content items in a content store are described. In one embodiment, the computer-implemented method includes maintaining a text index of content items in a content store to enable a keyword search on the content items, receiving a query having a keyword and generating a hit list from the text index using the keyword, and extracting frequent phrases from text within content items of the hit list. The computer-implemented method also includes assigning a relative relevance to the frequent phrases and grouping content items into topics based on presence of relevant phrases within the content items of the hit list. The hit list includes one or more content items of the content store. The frequent phrases having a relatively high relevance are relevant phrases.

System, method, and apparatus for multidimensional exploration of content items in a content store

The task of estimating the number of distinct values (DVs) in a large dataset arises in a wide variety of settings in computer science and elsewhere. The present invention provides synopses for DV estimation in the setting of a partitioned dataset, as well as corresponding DV estimators that exploit these synopses. Whenever an output compound data partition is created via a multiset operation on a pair of (possibly compound) input partitions, the synopsis for the output partition can be obtained by combining the synopses of the input partitions. If the input partitions are compound partitions, it is not necessary to access the synopses for all the base partitions that were used to construct the input partitions. Superior (in certain cases near-optimal) accuracy in DV estimates is maintained, especially when the synopsis size is small. The synopses can be created in parallel, and can also handle deletions of individual partition elements.

Method for estimating the number of distinct values in a partitioned dataset

A system and method for automatically discovering topical structures of databases includes a model builder adapted to compute various kinds of representations for the database based on schema information and data values of the database. A plurality of base clusterers is also provided, one for each representation. Each base clusterer is adapted to perform, for the representation, preliminary topical clustering of tables within the database to produce a plurality of clusters, such that each of the clusters corresponds to a set of tables on the same topic. A meta-clusterer aggregates results of the clusterers into a final clustering, such that the final clustering comprises a plurality of the clusters. A representative finder identifies representative tables from the clusters in the final clustering. The representative finder identifies at least one representative table for each of the clusters in the final clustering. The representative finder also arranges the representative tables by topic as a topical directory and outputs the topical directory.

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San Jose, CA, United States of America

John Sismanis