International Business Machines Corporation

Microsoft Technology Licensing, LLC

New H3c Technologies Co., Ltd.

Lin Luo

Yu Zhang

Shun Xiang Yang

Changying Sun

Graham John Wills

Da Ming Hao

Peter Kenneth Malkin

Peng Ji

Ya Bin Dang

Vugranam Chakravarthy Sreedhar

Mohammed Mostafa

Jizheng Xu

Shipeng Li

Feng Wu

Ying Li

Jing Luo

Xin Hui Li

Ruiqin Xiong

Martin Petitclerc

Bo Gao

Zhong Su

Shiwan Zhao

Ye Wy Wang

Vadakkedathu Thomas Rajan

Michelle Xue Zhou

Ling Shao

Changhua Sun

Ping Ping Cheng

Enliang Xu

Li Ni Zhang

Fei Wang

Jin Li

Rong Yan

Jian Zhang

Ya-Qin Zhang

Yuan He

Fan Jing Meng

Thomas Schaeck

Lawrence H Koved

Yue Chen

Yuan Yuan

Honglei Guo

Keke Cai

Qin Shi

Sachiko Yoshihama

Yunnan Wu

Naohiko Uramoto

Zhe Xiang

Lawrence Scott Rich

Chang Jie Guo

Masayoshi Teraguchi

Brent Tzion Hailpern

Jeffrey William Nichols

Harold Leon Ossher

Zhong Tian

Chun Ying

Chang Yan Chi

Wei Shang

Ken Chen

Shih-Gong Li

Guoyu Tang

Hang Jun Ye

Qinying Liao

Frederik De Keukelaere

John D Wiegand

Xiaowen He

Ying-xin Pan

Ming Su

Dave Thomson

A computer-implemented method, system and computer program product for providing predictions tailored for a specific domain. Each field within a set of datasets is classified with a concept defined in a domain specific ontology. After receiving a target field and identifying a target concept tagged to the target field, concepts that have an influential impact directly to the target concept or indirectly to a concept tagged to a field associated with the target field are identified. Fields are then identified as candidates for predictors from the identified concepts using a semantic relationship and a numeric association combined approach. An influential confidence score and a numeric analytics score are then calculated for each of these candidates upon which a final confidence score is computed. The identified candidates are ranked based on the final confidence scores, and the top ranked candidates for predictors are selected to build a statistical model.

Providing predictive analytics with predictions tailored for a specific domain

The present disclosure provides an IPv6 stateless address auto-configuration method, a network device and a host. The method includes: sending a router advertisement RA message to a host, wherein the RA message includes a prefix information option, and the prefix information option includes a prefix and a flag. The host performs IPv6 stateless address auto-configuration according to the prefix and the flag.

IPV6 stateless address auto-configuration

A computer-implemented method, system, and computer program product for generating a semantic model of data comprising a plurality of data items. Semantic concepts are identified for each data item in the plurality of data items. Data concepts are identified for each data item in the plurality of data items based on data characteristics of the data item and using a data ontology that defines the data concepts that are used to represent the data characteristics. The semantic model of the data is generated using the semantic concepts and the data concepts identified for the plurality of data items.

Holistic knowledge representation for semantic modeling of structured data

A computer-implemented method for joining data sets with mismatched geographic location naming conventions is provided. The method includes identifying, by the computer, a first dataset and a second dataset as join candidates. The method also includes joining, by the computer, the first dataset and the second dataset when, each row of the first user dataset is associated with a single geographic identifier using a geographic knowledge dataset that includes a geographic name lookup table and each row of the second user dataset is associated with a single geographic identifier using the geographic knowledge dataset, wherein the geographic name lookup table includes a plurality of alias names for each of a plurality of unique geographic locations.

Method to automatically join datasets with different geographic location naming conventions

Some embodiments include a method for identifying named entities in a question received in a question and answer system. The method can include receiving the question and constructing, via a named entity controller, a search query using all words in the question. The method can also include searching a named entity index for records that include named entity fields associated with certain of the words in the question. The method can also include determining a search score for each of the records based, at least in part, on how closely words in the question match a field of the record. The method can also include determining a weighted score for each of the records based on where the words in the question are positioned in the question. The method can also include creating a list of records including one or more of the records whose weighted score is above a threshold score. The method can also include providing the list of records for use in answering questions in the question and answer system.

Identifying named entities in questions related to structured data

A big data analytics system includes an event monitoring controller that detects a user targeted event from an initial digital query data flow. An event conversion controller determines at least one contextual element associated with the at least one user targeted event, and automatically converts the at least one contextual element into a search criteria. An external data query builder controller automatically executes an external search query based on the search criteria. A data fusing controller automatically fuses the at least one contextual element generated from the at least one user targeted event, together with external search results generated from the external search query and the initial query data flow to generate a fused data file.

Automatically connecting external data to business analytics process

Methods, computer program products, and systems are presented. The methods include, for instance: metadata on respective datasets from a plurality of data sources. An enhanced metadata is prepared with semantic information, available data statistics, and search index associated with items of the metadata. The metadata is also classified based on ontology rules. An intent phrase to search the datasets from the plurality of data sources is obtained from a user. The enhanced metadata is searched with a semantic search query formed from the intent phrase, and search result is classified into semantic element classes generated from the intent phrase. A metadata model that satisfies the intent phrase is produced by identifying join columns and respective join relationships from the search result.

Heuristic dimension reduction in metadata modeling

A computer-implemented method, system, and computer program product for classifying a concept of a data item. A data item label for the data item is received. The data item label is analyzed using a natural language processing tool to generate additional lexical information for the data item label. A search query is built using the additional lexical information for the data item label. The search query is used to search a clue concept index to produce a search result. The clue concept index comprises clue concept records that identify clues for concepts. The search result identifies clue concept records from the clue concept index that match the search query. A concept is selected from the clue concept records identified in the search result as the concept for the data item.

Disambiguation of concept classifications using language-specific clues

Techniques for generating a personality trait model are described. According to an example, a system is provided that can generate text data and linguistic data, and apply psycholinguistic data to the text data and the linguistic data, resulting in updated text data and updated linguistic data. The system is further operable to combine the updated text data with the updated linguistic data to generate a personality trait model. In various embodiments, the personality trait model can be trained and updated as additional data is received from various inputs.

Predicting personality traits based on text-speech hybrid data

Techniques are provided for automated privacy scoring of user information. In one example, a system comprises a memory that stores computer executable components, and a processor that executes computer executable components stored in the memory. The computer executable components can comprise a privacy scoring component that employs a privacy identification model to generate a privacy score for a user and a product in the particular context based on information associated with the user and the product in the particular context. The computer executable components can also comprise a privacy enforcement component that implements one or more privacy features on the information based on the privacy score.

Determining privacy for a user and a product in a particular context

A computer-implemented method for executing a query specification can include evaluating the query specification by a first computing device to identify a candidate expression in the query specification to be executed by a second computing device hosting a source of data specified in the candidate expression. A cost estimate for executing the candidate expression at the first computing device is determined. Whether the cost estimate of executing the candidate expression at the first computing device is greater than a threshold is determined. The candidate expression is replaced with a native query function when the cost estimate of executing the candidate expression at the first computing device is greater than the threshold. The native query function can be executed at the second computing device. It can be determined whether a runtime context is necessary, and if necessary, a context parameter is injected into the native query function.

Distributed execution of expressions in a query

At least one of an HTTP request message and an HTTP response message is intercepted. A corresponding HTTP message model includes a plurality of message model sections. A representation of the at least one of an HTTP request message and an HTTP response message is parsed into message sections in accordance with the message model sections of the HTTP message model. A plurality of security rules are bounds to the message model sections. The plurality of security rules each specify at least one action to be taken in response to a given condition, which is based, at least in part, on a corresponding given one of the message sections. The at least one of an HTTP request message and an HTTP response message is processed in accordance with the plurality of security rules. Techniques for developing rules for a web application server firewall are also provided.

Hierarchical rule development and binding for web application server firewall

A method, apparatus and computer program product for performing authorization control in a cloud storage system. The method comprises: receiving an access request to a file block, wherein the file block is embedded with tag data comprising at least file block authorization information; retrieving the file block; extracting the file block authorization information from the tag data; determining whether the access request matches the file block authorization information; and performing the access request if the access request matches the file block authorization information. Effective authorization control may be performed in a cloud storage system.

Performing authorization control in a cloud storage system

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Beijing, China

Lin Luo