At&t Intellectual Property I, L.p.

At+t Corp.

Columbia University

Lucent Technologies Inc.

University of Texas System

Emory University

Other

University of Michigan

Rutgers, the State University of New Jersey

National Science Foundation

Divesh Srivastava

Nikolaos Koudas

Kadangode K Ramakrishnan

Balachander Krishnamurthy

Graham Cormode

Yaron Kanza

Yin Sheng Zhang

Hosagrahar Visvesvaraya Jagadish

Michael Rabinovich

Guy Jacobson

Nick Koudas

Tamraparni Dasu

Tae Won Cho

Theodore Donald Johnson

Marios Hadjieleftheriou

Vladislav Shkapenyuk

Sihem Amer-Yahia

Ioannis Kotidis

Philip Korn

Shanmugavelayutham Muthukrishnan

Oliver Spatscheck

David Gerald Belanger

Nicolas Bruno

Gisli Hjalmtysson

William Christopher Fenner

Mark Alan Jones

Charles D Cranor

Alexandros Biliris

Euthimios Panagos

Suresh Venkatasubramanian

Cecilia M Procopiuc

Xin Dong

Eleftherios Koutsofios

Rittwik Jana

Inderpal Singh Mumick

Dan Suciu

Sundararajarao Sudarshan

Lukasz Golab

Alon Yitzchak Levy

Maria F Fernandez

Rajat Malik

Sudipto Guha

Howard Karloff

Samrat Bhattacharjee

Raymond Ng

Jignesh M Patel

Yuqing Wu

Themistoklis Palpanas

Shurug Ali Al-Khalifa

Panayiotis Tsaparas

Vijay Gopalakrishnan

Howard Paul Katseff

Michail Vlachos

Kenneth Andrew Ross

Li Xiong

Deborah F Swayne

Smriti Bhagat

Yu Xu

Su Chen

Lakshmanan Sundaram Viravanallur

Ioannis Velegrakis

Gordon Woodhull

Rui Zhang

Peter J Stuckey

David Toman

SungRan Cho

Nikolas Koudas

Dimitrios Gunopulos

Olga Kapitskaia

Teddy Chu

Shuaicheng Ma

Dongwon Lee

Dimitra Vista

Erian Laperi

Jeremy A Dilks

Ting Yu

Qing Zhang

Robert J Hall

Charles Robert Kalmanek, Jr

Steven Michael Bellovin

Ke Yi

Xiaohui Yu

Peter G Selfridge

Joshua Whitney

Thomas Kirk

Chih Chien Huang

Johan Muedsam

Aarthi Reddy

Amélie Marian-Guerrier

Matt Dugan

Xuan Liu

George Kollios

Feifei Li

Jean Luo

Vijay Gopalkrishnan

Laure Berti-Equille

Bing Tian Dai

Beng Chin Ooi

Kathleen McKenna

Nikolacs Koudas

Aspects of the subject disclosure may include, for example, detecting data streams by a processing system including a processor, wherein the processing system is associated with an anomaly detection and alerting system in which stream processing and model maintenance is decoupled from one another, and wherein one or more dedicated virtual machines (VMs) store and maintain anomaly detection and alerting models, based on the detecting, causing, by the processing system, a plurality of stream-processing VMs to be instantiated for processing the data streams, and managing, by the processing system, data stream assignments for the plurality of stream-processing VMs based on monitoring of one or more conditions, wherein the plurality of stream-processing VMs process assigned data streams by executing instances of the anomaly detection and alerting models, and provide model outputs to the one or more dedicated VMs for updating of the anomaly detection and alerting models. Other embodiments are disclosed.

System and method for cloud-based anomaly detection and alerting for streaming data

Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, define operations of modules that monitor a computational hierarchy, the modules include a first module for generating base alerts with a hierarchical key associated with an operation of the computational hierarchy and for rolling up the base alerts based on the computational hierarchy, resulting in rolled-up base alerts; a second module for generating super alerts from a concentration of the rolled up base alerts; and a third module comprising a first plurality of models that selects the super alerts based on priority, persistence of anomalies, pervasiveness of the super alerts generated, recency, or a combination thereof, generates a smart alert based on voting on the super alerts selected by the first plurality of models, and presents the smart alert on a user interface. Other embodiments are disclosed.

Architecture for scalable smart alerting across a multitude of data streams

Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving a series of values from a data stream generated by one or more equipment in a communications network; creating a first window of values received before a first point in time and a second window of values received on or after the first point in time; comparing a distribution of values in the first window and values in the second window to compute a distance at the first point in time; repeating the creating and comparing at subsequent points in time after the first point in time, thereby generating a series of distances; computing a z-score for a first distance in the series of distances; generating an alert responsive to the z-score exceeding a threshold; and storing the alert in a log of alerts. Other embodiments are disclosed.

System and method for detecting anomalous changes in data streams and generating textual explanations

A method performed by a processing system including at least one processor includes applying a contextual filter to mask a portion of at least one of: an input of a software application, an output of the software application, or an underlying dataset of the software application, where the contextual filter simulates a limitation of a user of the software application, executing the software application with the contextual filter applied to the at least one of: the input of the software application, the output of the software application, or the underlying dataset of the software application, collecting ambient data during the executing, and recommending, based on a result of the executing, a modification to the software application to improve at least one of: an accessibility of the software application or an inclusion of the software application.

Testing accessibility of geospatial applications using contextual filters

Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including receiving a call; selecting a next carrier to handoff the call; generating a call data record (CDR) for the handoff to the next carrier; encrypting the CDR using a call encryption key, thereby creating an encrypted CDR; encrypting the encrypted CDR using a committee encryption key, thereby creating a double encrypted CDR; recording the double encrypted CDR to a blockchain; and sending the call encryption key to the next carrier. Other embodiments are disclosed.

System and method for mitigating international revenue share fraud

A processing system including at least one processor may obtain at least one search term and an identity of a searcher, identify individuals who are each either a one-step or two-step association of the searcher, identify first documents associated with the individuals and associated with the search term(s), calculate, for each individual, a score based upon citations of second documents to documents associated with the individual from among the first documents, generate an invitation to at least one individual to appear in a search result, and obtain a consent from the at least one individual. The processing system may then present the search result as an ordered list of at least a portion of the individuals, where the ordered list is ordered by the score for each individual, and where the at least the portion of the plurality of individuals includes the at least one individual.

Confidentiality preserving intraorganizational expert search

Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving a first record of sending a file and a hash for the file from a sender of the file; recording the first record on a blockchain; providing the first record to a receiver of the file; and monitoring a predetermined time period in which to receive a second record of receiving the file and the hash for the file. Other embodiments are disclosed.

Blockchain-powered ledger for a data supply chain

Architectures and techniques are presented that can more efficiently update derived data products in response to updated source data. Source data is typically stored in source tables, whereas a materialized view of a query can generate a derived table based on the state of the source tables at the time the query is executed. When source data changes (e.g., in response to late-arriving input data), rather than recomputing the entire derived table (e.g., by again executing the original query, which can be expensive), an invertible relationship between timestamps can be leveraged to identify only those portions of the derived table that are affected by the update. Therefore, a new defining query can be generated to update only those portions of the derived table that are affected by the source data update.

Update propagation in a data stream warehouse

Aspects of the subject disclosure may include, for example, responsive to a viewing request associated with a media content item, for each original content segment of a plurality of content segments of the media content item determining whether content characteristic information associated with the original content segment is compatible with viewing context information associated with the viewing request, and, if not compatible, then replacing the original content segment with an alternative content segment of the plurality of content segments, and streaming the alternative content segment to a user device, and, if compatible, then responsive to determining the content characteristic information is compatible with the viewing context information, streaming the original content segment to the user device. Other embodiments are disclosed.

Method and apparatus for adjusting streaming media content based on context

Systems and methods utilize a data model database which includes a plurality of symbol data types. Each of the plurality of symbol data types have one or more symbol data fields. The data model database further includes a plurality of concrete data types. Each of the concrete data types have one or more language-agnostic concrete fields associated with each of the one or more symbol data fields. Each of the one or more language-agnostic concrete fields apply one or more concrete constraints to each of the corresponding symbol data fields. The data model database further includes a plurality of carrier data types. The plurality of carrier data types having one or more language-specific carrier fields associated with each of the one or more language-agnostic concrete fields. Each of the one or more language-specific carrier fields apply one or more carrier constraints to each of the corresponding language-agnostic concrete fields.

Data model database

A method and apparatus for enriching metadata are disclosed. For example, the method implemented via a processor monitors metadata associated with a first webpage of a plurality of webpages, the first webpage having been determined to be similar to a second webpage of the plurality of webpages, detects a change to the metadata associated with the first webpage, determines whether the change to the metadata associated with the first webpage invokes an update to a metadata associated with the second webpage, and processes the update of the metadata associated with the second webpage when the change invokes the update to the metadata associated with the second webpage.

Method and apparatus for enriching metadata via a network

Concepts and technologies disclosed herein are directed to location-based blockchain. A localized corroborator system can receive, from a user device, an initiation message including a public key, and in response, can generate and send the random session identifier to the user device. The system can receive, from the user device, a signed session identifier including the random session identifier signed by the user device using a private key. The system can check a time that elapsed between sending the random session identifier to the user device and receiving the signed session identifier from the user device. The system can verify authenticity of the signed session identifier using the public key. In response to the time being less than a pre-defined threshold, the system can issue a location certificate to the user device. The user device can utilize the location certificate to conduct a certified blockchain transaction in a blockchain.

Location-based blockchain

A system for providing continuous monitoring of data quality in a dynamic feed environment is disclosed. In particular, the system utilizes a feed inspection tool to detect anomalies in data gathering detected from feed metadata and anomalies in data measurement detected based on file contents. In order to do so, the feed inspection tool may aggregate, for a plurality of aggregation intervals, data feeds and associated metadata feeds. Once the data feeds and metadata feeds are aggregated, the feed inspection tool may generate, for a baseline model feed, baseline statistical models by utilizing historical data of the aggregated feeds in sliding windows of different lengths. The feed inspection tool may then identify, for a plurality of monitoring time delays, data outliers by comparing the aggregated feeds with the baseline model feed. A data quality feed based on the data outliers identified may then be generated and published.

System for continuous monitoring of data quality in a dynamic feed environment

Growing community of inventors

Summit, NJ, United States of America

Divesh Srivastava