Amazon Technologies, Inc.

International Business Machines Corporation

Microsoft Technology Licensing, LLC

At&t Intellectual Property I, L.p.

Lucent Technologies Inc.

Sprint Communications Comapny L.p.

Christos Faloutsos

Rakesh Agrawal

Anurag Windlass Gupta

Myron Dale Flickner

Yossi Matias

Abraham Silberschatz

Ramakrishnan Srikant

Yannis Papakonstantinou

Gerald George Kiernan

Paul S Bradley

Vuk Ercegovac

Usama M Fayyad

Roberto Javier Bayardo

Michail Petropoulos

Ralf Rantzau

Hanghang Tong

Stavros Harizopoulos

Yehuda Koren

Sridhar Machiraju

Balakrishnan Narayanaswamy

Arun N Swami

Ashwin Sridharan

James Christopher Schneider

Demetrios Achlioptas

William Robinson Equitz

Panagiotis Parchas

Matthew Butler

Mukund Seshadri

Bodicherla Aditya Prakash

Andrey Kan

Xin Luna Dong

Mina Loghavi

Yi Fan

Queries that reference materialized views may be rewritten to compensate for stale materialized views. A query may be received that references a materialized view. A determination may be made as to whether the materialized view is stale. For a stale materialized view, the query may be rewritten to generate a query plan that obtains changes made to base tables for the materialized view not included in the stale materialized view and considers the change(s) when generating a result for the query from the materialized view. The rewritten query may then be performed to provide a result as if the materialized view were up-to-date.

Rewriting queries to compensate for stale materialized views

Using a graph representation of join history may be performed to distribute database data. Join history may be collected, captured, or tracked which describes the history of join operations between columns of different tables in a database. A graph representation of the join history may be generated. The graph representation may indicate a likelihood of different joins that may be performed between the columns of the tables of a database. An evaluation of the join history may be performed to identify columns for tables in the database to distribute the data of the tables amongst multiple storage locations according to the identified columns.

Using a graph representation of join history to distribute database data

Devices and techniques are generally described for fraud detection. A machine learning model is used to determine a first fraud risk score for a first transaction. The machine learning model includes a first set of weights. A first covariance matrix is determined for the machine learning model based at least in part on the first fraud risk score. A second set of weights for the machine learning model is determined. The second set of weights is determined based on the first set of weights and the first covariance matrix. In various examples, the machine learning model with the second set of weights is used to determine a second fraud risk score for a second transaction. A fraud decision surface is determined and the second fraud risk score is compared to the fraud decision surface. Data indicating that the second transaction is fraudulent is sent to a computing device.

Machine learning optimization for fraud detection

An improved user interface in which related items are grouped intuitively, and ultimately results in the number of navigational steps performed by the user to identify desired related items being reduced, is described herein. For example, instead of having a user interface display a single view that includes all related items, a recommendation system groups the related items based on their respective attribute values and produces data that results in a user interface that displays the related items in these groups. The recommendation system generates labels for these groups such that a user can clearly identify what types of related items are included therein. Thus, a user would not have to browse through a single list of related items ordered in an unfamiliar manner. Rather, a user can browse through smaller subsets of related items, and only in those groups that correspond with attribute values of interest to the user.

User interface for navigating related items

What is disclosed is a method of operating a communication network, where the communication network comprises a set of users. The method includes obtaining usage activity for each of the users of the set and processing the usage activity to determine connectivity information between individual ones of the users of the set. The method also includes processing the connectivity information to determine users of the set with similar connectivity, and processing the users of the set with similar connectivity to identify a community of users within the users of the set with similar connectivity. The method also includes monitoring at least one of the users in the community to determine if action is needed for at least another one of the users in the community.

Determining user communities in communication networks

A method and system for graph mining direction-aware proximity measurements. A directed graph includes nodes and directed edges connecting the nodes. A direction-aware proximity measurement is calculated from a first node to a second node or from a first group of nodes to a second group of nodes. The direction-aware proximity measurement from a first node to second node is based on an escape probability from the first node to the second node. Disclosed herein are methods for efficiently calculating one or multiple direction-aware proximity measurements. The direction-aware proximity measurements can be used in performing various graph mining applications.

Direction-aware proximity for graph mining

An auditing framework for determining whether a database disclosure of information adhered to its data disclosure policies. Users formulate audit expressions to specify the (sensitive) data subject to disclosure review. An audit component accepts audit expressions and returns all queries (deemed 'suspicious') that accessed the specified data during their execution.

Auditing compliance with a hippocratic database

Method of reducing dimensionality of a set of attributes used to characterize a sparse data set

The invention concerns a method for estimating characteristic information of data items in a data set, such as a database, based on parameters of a multifractal distribution. The invention facilitates efficient estimation of such characteristic information of data contained in a data set more accurately than known estimation methods and without requiring an exhaustive analysis of the data. The invention also concerns an efficient technique for generating the parameters for the multifractal distribution.

Method for characterizing information in data sets using multifractals

A high dimensional indexing method is disclosed which takes a set of objects that can be viewed as N-dimensional data vectors and builds an index which treats the objects like k-dimensional points. The method first defines and applies a set of feature extraction functions that admit some similarity measure for each of the stored objects in the database. The feature vector is then transformed in a manner such that the similarity measure is preserved and that the information of the feature vector v is concentrated in only a few coefficients. The entries of the feature vectors are truncated such that the entries which contribute little on the average to the information of the transformed vectors are removed. An index based on the truncated feature vectors is subsequently built using a point access method (PAM). A preliminary similarity search can then be conducted on the set of truncated transformed vectors using the previously created index to retrieve the qualifying records. A second search on the previously retrieved set of vectors is used to eliminate the false positives and to get the results of the desired similarity search.

Method for high-dimensionality indexing in a multi-media database

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Pittsburgh, PA, United States of America

Christos Faloutsos