Abstract: A document is received that has a plurality of lines with text. This document includes text associated with at least one topic of interest and text not associated with the at least one topic of interest. Thereafter, it is determined, for each line in the document, a length of the line and a number of off-topic indicators with the off-topic indicators characterizing portions of the document as likely being not being associated with the at least one topic of interest. Thereafter, a density for each line can be determined based on the determined line length and the determined number of off-topic indicators. The determined densities for each line are used to identify portions of the documents likely associated with the at least one topic of interest so that data characterizing the identified portions of the document can be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: Techniques for information retrieval include the features of receiving a plurality of data records, updating a plurality of database records associated with the received plurality of data records stored in a database, receiving a query for a particular database record, and preparing for display, in response to the query, one or more of the database records based on at least one of the name of the business enterprise or the alias associated with the name of the business enterprise. Each data record includes data fields associated with a business enterprise. The data fields include a name of the business enterprise. Each database record includes attributes including the name of the business enterprise and an alias associated with the name of the business enterprise. The query includes at least one of the name of the business enterprise or the alias associated with the name of the business enterprise.

Abstract: Techniques for data classification include matching one or more attributes of a commodity with one or more terms of a plurality of terms in a word matrix; generating, based on the matching, a vector for the commodity; and identifying, based on the vector, one or more classification regions that each define a classification of the commodity.

Abstract: To retrieve a sequence of associated events in log data, a request expression is parsed to retrieve types of dependencies between events which are searched, and the constraints (e.g., keywords) which characterize each event. Based on the parsing results, query components can be formed, expressing the constraints for individual events and interrelations (e.g., time spans) between events. A resultant span query comprising the query components can then be run against an index of events, which encodes a mutual location of associated events in storage.

Abstract: Techniques for data classification include matching one or more attributes of a commodity with one or more terms of a plurality of terms in a word matrix; generating, based on the matching, a vector for the commodity; and identifying, based on the vector, one or more classification regions that each define a classification of the commodity.

Abstract: A document is received that has a plurality of lines with text. This document includes text associated with at least one topic of interest and text not associated with the at least one topic of interest. Thereafter, it is determined, for each line in the document, a length of the line and a number of off-topic indicators with the off-topic indicators characterizing portions of the document as likely being not being associated with the at least one topic of interest. Thereafter, a density for each line can be determined based on the determined line length and the determined number of off-topic indicators. The determined densities for each line are used to identify portions of the documents likely associated with the at least one topic of interest so that data characterizing the identified portions of the document can be provided. Related apparatus, systems, techniques and articles are also described.

Abstract: A company is associated, in an enterprise resource planning system, with a plurality of business entities that each have at least one structured record used by the enterprise resource planning system to characterize the business entity. Thereafter, documents are obtained from a plurality of information sources that characterize events associated with each business entity. It is then determined, using pre-defined business rules, which of the events are pertinent to the company so that enhancement records can be generated for the events determined to be pertinent to the company. These enhancement records characterize the corresponding event and are linked to the structured record for the corresponding business entity. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that comprises an entity name. Thereafter, it is determined (i) whether there are any punctuation variations for the entity name, (ii) whether there is at least one character to drop from the entity name, and (iii) whether there are alternative equivalents of at least a portion of the entity name. After such determinations have been made, a plurality of variants for the entity name is generated based on a combination of each determined punctuation variation, determined at least one character to drop, and determined alternative equivalent. Related apparatus, systems, techniques and articles are also described.

Abstract: Techniques for data classification include receiving, at a local computing system, a query from a remote computing system, the query comprising data associated with a commodity, the data comprising one or more attributes of the commodity; matching the one or more attributes of the commodity with one or more terms of a plurality of terms in a word matrix that includes a plurality of nodes that each include a term of the plurality of terms and a plurality of links that each connect two or more nodes and define a similarity between the two or more nodes; generating, based on the matching, a numerical vector for the business enterprise commodity; identifying one or more classification regions that each define a classification of the commodity; and preparing the classifications for display at the remote computing system.

Abstract: Techniques for data classification include receiving, at a local computing system, a query from a remote computing system, the query comprising data associated with a commodity, the data comprising one or more attributes of the commodity; matching the one or more attributes of the commodity with one or more terms of a plurality of terms in a word matrix that includes a plurality of nodes that each include a term of the plurality of terms and a plurality of links that each connect two or more nodes and define a similarity between the two or more nodes; generating, based on the matching, a numerical vector for the business enterprise commodity; identifying one or more classification regions that each define a classification of the commodity; and preparing the classifications for display at the remote computing system.

Abstract: Techniques for information retrieval include receiving a plurality of data records, each data record including data fields associated with a business enterprise, the data fields including a name of the business enterprise; updating a plurality of database records associated with the received plurality of data records stored in a database, each database record including attributes including the name of the business enterprise and an alias associated with the name of the business enterprise; receiving a query for a particular database record, the query including at least one of the name of the business enterprise or the alias associated with the name of the business enterprise; and preparing for display, in response to the query, one or more of the database records based on at least one of the name of the business enterprise or the alias associated with the name of the business enterprise.

Abstract: To retrieve a sequence of associated events in log data, a request expression is parsed to retrieve types of dependencies between events which are searched, and the constraints (e.g., keywords) which characterize each event. Based on the parsing results, query components can be formed, expressing the constraints for individual events and interrelations (e.g., time spans) between events. A resultant span query comprising the query components can then be run against an index of events, which encodes a mutual location of associated events in storage.

Abstract: Event data (e.g., log messages) are represented as sets of attribute/value pairs. An index maps each attribute/value pair or attribute/value tuple to a pointer that points to event data which contains the attribute/value pair or attribute/value tuple. An attribute co-occurrence map or matrix can be generated that includes attribute names that co-occur together. Queries and custom reports can be generated by projecting event data into one or more attributes or attribute/value pairs, and then determining statistics on other attributes using a combination of the inverted index, the attribute co-occurrence map or matrix, operations on sets and/or math and statistical functions.

Abstract: To retrieve a sequence of associated events in log data, a request expression is parsed to retrieve types of dependencies between events which are searched, and the constraints (e.g., keywords) which characterize each event. Based on the parsing results, query components can be formed, expressing the constraints for individual events and interrelations (e.g., time spans) between events. A resultant span query comprising the query components can then be run against an index of events, which encodes a mutual location of associated events in storage.

Abstract: Event data (e.g., log messages) are represented as sets of attribute/value pairs. An index maps each attribute/value pair or attribute/value tuple to a pointer that points to event data which contains the attribute/value pair or attribute/value tuple. An attribute co-occurrence map or matrix can be generated that includes attribute names that co-occur together. Queries and custom reports can be generated by projecting event data into one or more attributes or attribute/value pairs, and then determining statistics on other attributes using a combination of the inverted index, the attribute co-occurrence map or matrix, operations on sets and/or math and statistical functions.

Abstract: Event data (e.g., log messages) are represented as sets of attribute/value pairs. An index maps each attribute/value pair or attribute/value tuple to a pointer that points to event data which contains the attribute/value pair or attribute/value tuple. An attribute co-occurrence map or matrix can be generated that includes attribute names that co-occur together. Queries and custom reports can be generated by projecting event data into one or more attributes or attribute/value pairs, and then determining statistics on other attributes using a combination of the inverted index, the attribute co-occurrence map or matrix, operations on sets and/or math and statistical functions.

Abstract: To retrieve a sequence of associated events in log data, a request expression is parsed to retrieve types of dependencies between events which are searched, and the constraints (e.g., keywords) which characterize each event. Based on the parsing results, query components can be formed, expressing the constraints for individual events and interrelations (e.g., time spans) between events. A resultant span query comprising the query components can then be run against an index of events, which encodes a mutual location of associated events in storage.

Abstract: Event data (e.g., log messages) are represented as sets of attribute/value pairs. An index maps each attribute/value pair or attribute/value tuple to a pointer that points to event data which contains the attribute/value pair or attribute/value tuple. An attribute co-occurrence map or matrix can be generated that includes attribute names that co-occur together. Queries and custom reports can be generated by projecting event data into one or more attributes or attribute/value pairs, and then determining statistics on other attributes using a combination of the inverted index, the attribute co-occurrence map or matrix, operations on sets and/or math and statistical functions.

Abstract: Embodiments of a shared resource distributed index mechanism that peers in a peer-to-peer network may utilize to distribute index entries corresponding to resources to indexes of shared resources among one or more other peers. These indexes may be used to direct queries to peers where the queries are most likely to be answered. When a query is received by a rendezvous peer including one or more indexes, contents of the query may be “looked up” in the index to find matches. The results of the lookup may include information on one or peer(s) that may hold advertisement(s) to the resource requested by the query. The query may then be forwarded to one or more peers that may hold the advertisement for the resource. Embodiments may provide “loosely-coupled” distribution of index entries for use in querying for resources in the peer-to-peer network.

Abstract: Systems and methods for resolving search queries to information providers in a distributing search network. In a network including nodes generating search requests and nodes providing information, a node may operate as hub to route search requests from requesting nodes to provider nodes. Providers may register with a network hub. Registration information may include address information and data indicating the queries or type of queries for which that provider may have relevant data. A hub may resolve search queries against provider registrations to determine a set of providers to which to route each search query. Several systems and methods of selecting some of the providers are described, including the use of bidding, ranking, and statistical data.