Infosys Technologies Limited

Infosys Limited

Srikumar Krishnamoorthy

Prahalad Haldhoderi Achutharao

Radha Krishna Pisipati

Sumit Kumar Bose

Abdul Sakib Mondal

Aakanksha Gagrani Singh

Krupa Benhur Gadde

Shanmugavel Sadasivam

Kishore Indukuri Varma

Aakanksha Gagrani

Nilesh Dilip Ranade

Avdhoot Kishore Saple

Brijesh Pillai

Durga Prasad Muni

A natural language query tool comprising cascaded conditional random fields (CRFs) (e.g., a linear-chain CRF and a skip-chain CRF applied sequentially) processes natural language input to produce output that can be used in database searches. For example, cascaded CRFs extract entities from natural language input that correspond to column names or column values in a database, and identify relationships between the extracted entities. A search engine can execute queries based on output from the cascaded CRFs over an inverted index of a database, which can be based on one or more materialized views of the database. Results can be sorted (e.g., according to relevance scores) and presented in a user interface.

Natural language querying with cascaded conditional random fields

A method and system for implementing automatic recovery from failure of resources in a grid-based distributed database is provided. The method includes determining the category of each node in the subgroup of nodes, where the determination identifies each node as at least one of a worker node and an idle node. The method further includes saving state of each worker node engaged in execution of a task in a shared memory at pre-determined time intervals. Each worker node is monitored by one or more idle nodes in each sub-group. Upon detection of no change in state of worker node for a pre-determined period of time, a failure notification is raised by one or more idle nodes that have detected failure of the worker node.

Method and system for automatic failover of distributed query processing using distributed shared memory

Computing resources can be assigned to sub-plans within a query plan to effect parallel execution of the query plan. For example, computing resources in a grid can be represented by nodes, and a shortest path technique can be applied to allocate machines to the sub-plans. Computing resources can be provisionally allocated as the query plan is divided into query plan segments containing one or more sub-plans. Based on provisional allocations to the segments, the computing resources can then be allocated to the sub-plans within respective segments. Multiprocessor computing resources can be supported. The techniques can account for data locality. Both pipelined and partitioned parallelism can be addressed. Described techniques can be particularly suited for efficient execution of bushy query plans in a grid environment. Parallel processing will reduce the overall response time of the query.

Allocating resources for parallel execution of query plans

A method and system for parallel query processing is provided. The method includes estimating a number of sub-queries that may be formed corresponding to an input query. The method further includes fragmenting the input query into a plurality of sub queries by adding one or more filter predicates to the input query without altering the semantics of the input query. Further, processing of the sub queries is scheduled. Thereafter, one or more query execution plans are generated and processed. Following the processing of the query execution plans, the results of the processed sub queries are collated, aggregated and presented in a required format.

System and method for parallel query evaluation

A system and method for matching one or more source schemas with one or more target schemas is provided. The matching between source and target schemas is performed by gathering inputs pertaining to the source and target schemas, wherein the inputs comprises a set of details in a predefined format. Thereafter, the gathered inputs are processed by comparing the source schemas with the target schemas. The processing is performed to identify a set of matches between the source and target schemas based on the linguistic similarity, structural similarity and functional similarity and relationship between the source and target schemas. Subsequently, the identified matches are stored.

System and method for schema matching

A method for processing a distributed query in a network of computational resources is provided. The method includes receiving a user-defined freshness criterion and a distributed query from a user. The user-defined data freshness criterion is based on the version of the distributed query results desired by the user. An integrated cost model is formulated to optimize the execution of the distributed query. The integrated cost model is based on one or more integrated cost model factors. Thereafter, an objective function is constructed, based on the processing cost for each of the one or more copies of the one or more relations, and a data transmission cost for the transfer of the one or more copies of the one or more relations from a first to a second computational resource. Subsequently, an optimal solution of the objective function is calculated by using one or more heuristic approaches.

Integrated distributed query processor for data grids

A method of storing semantically similar documents on proximally located peers in a structured peer to peer overlay network, where each peer is assigned a unique identifier and each document includes one or more words belonging to at least one hierarchical structured collection of words. A method of searching and retrieving documents, corresponding to a search query, from a structured peer to peer overlay network is also provided.

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Chennai, India

Srikumar Krishnamoorthy