Microsoft Technology Licensing, LLC

Karthik Kalyanaraman

Trishul A Chilimbi

Knut Magne Risvik

John Grant Bennett

Michael Joseph Hopcroft

Vishesh Parikh

Jan O Pedersen

Chad P Walters

Hui Shen

Johnson T Apacible

Arshish C Kapadia

Tarkan Sevilmis

Yutaka Suzue

Yuxiong He

Christopher Walter Anderson

Olatunji Ruwase

Henry Setiawan

Feng Yan

Guang-an Wu

Mike Hopcroft

Nazeeruddin Mohammed

Methods and computer storage media are provided for generating entries for documents in a forward index. A document and its document identification are received, in addition to static features that are query-independent. The document is parsed into tokens to form a token stream corresponding to the document. Relevant data used to calculate rankings of document is identified and a position of the data is determined. The entry is then generated from the document identification, the token stream of the document, the static features, and the positional information of the relevant data. The entry is stored in the forward index.

Efficient forward ranking in a search engine

A performance investigation tool (PIT) is described herein for investigating the performance of a distributed processing system (DPS). The PIT operates by first receiving input information that describes a graph processing task to be executed using a plurality of computing units. The PIT then determines, based on the input information, at least one time-based performance measure that describes the performance of a DPS that is capable of performing the graphical task. More specifically, the PIT can operate in a manual mode to explore the behavior of a specified DPS, or in an automatic mode to find an optimal DPS from within a search space of candidate DPSs. A configuration system may then be used to construct a selected DPS, using the plurality of computing units. In one case, the graph processing task involves training a deep neural network model having a plurality of layers.

Tool for investigating the performance of a distributed processing system

A search index includes tiered posting lists. Each posting list in the search index corresponds with a different atom and includes a list of documents containing the particular document. Additionally, a rank is stored with each document listed in a posting list for a given atom representing the relevance of the atom to the context of each document. At least some of the posting lists in the search index are tiered. A tiered posting list is divided into a number of tiers with the tiers being ordered by document while each tier is internally ordered by document. Employing tiered posting lists within the search index allows a search engine to evaluate search queries in a manner that allows for a number of efficiencies and precise stopping.

Tiering of posting lists in search engine index

Methods and systems are provided for using a hybrid-distribution system to identify relevant documents based on a search query. A group of documents is assigned to a particular segment. The group of documents is indexed both by atom and by document to form a reverse index and a forward index. Both indexes are divided amongst each node in that segment so that each node is responsible for storing and accessing a different portion of both the reverse and forward indexes. The reverse index portion is accessed on each of a first set of nodes to identify a first set of documents that is relevant to a particular search query. Document identifications associated with the first set of documents are used to identify a second set of nodes that access their forward index portions to limit the number of relevant documents to a second set of documents.

Hybrid-distribution model for search engine indexes

Methods are provided for populating search indexes with atoms identified in documents. Documents that are to be indexed are identified, and for each document, atoms are identified and are categorized as unigrams, n-grams, and n-tuples. A list of atom/document pairs is generated such that an information metric can be computed for each pair. An information metric represents a ranking of the atom in relation to the particular document. Based on the information metric, some atom/document pairs are discarded and others are indexed.

Selection of atoms for search engine retrieval

A preliminary segment root and a final segment root are selected for each segment. Each time a search query is received, a set of nodes in each segment that will be used to resolve the search query is identified. A preliminary segment root is selected from the set of nodes. Based on statistical data from each node in the set of nodes indicating each node's capability to act as a final segment root that assembles query-execution data, the preliminary segment root algorithmically selects the final segment root. The other nodes in the set of nodes are notified regarding the identity of the final segment root.

Dynamic query master agent for query execution

Methods and computer storage media are provided for generating an algorithm used to provide preliminary rankings to candidate documents. A final ranking function that provides final rankings for documents is analyzed to identify potential preliminary ranking features, such as static ranking features that are query independent and dynamic atom-isolated components that are related to a single atom. Preliminary ranking features are selected from the potential preliminary ranking features based on many factors. Using these selected features, an algorithm is generated to provide a preliminary ranking to the candidate documents before the most relevant documents are passed to the final ranking stage.

Decomposable ranking for efficient precomputing

A priority hash index provides efficient lookup of posting lists for search query terms. The priority hash index is a data structure in which hash values for terms are distributed across multiple storage devices based on importance of the terms and access speeds of the storage devices. Terms are grouped into search lists with each search list including a storage location on each storage device. When a search query is received, a term is identified and hashed to a location on the first storage device and to generate a unique hash value for the term. The locations on the storage device for the term's search list are sequentially read until the hash value for the term is located to access the posting list for the term.

Priority hash index

Search results are identified and returned in response to search queries by evaluating and pruning candidate documents in multiple stages. The process employs a search index that indexes atoms found in documents and pre-computed scores for document/atom pairs. When a search query is received, atoms are identified from the search query and a reformulated query is generated based on the identified atoms. The reformulated query is used to identify matching documents, and a preliminary score is generated for matching documents using a simplified scoring function and pre-computed scores in the search index. Documents are pruned based on preliminary scores, and the remaining documents are evaluated using a final ranking algorithm that provides a final set of ranked documents, which is used to generate search results to return in response to the search query.

Matching funnel for large document index

A line of business data integration ('LOBI') stack is provided that is configured to store an abstract representation of a mechanism utilized by a line of business ('LOB') system to return requested LOB data in batches. When a request is received from a client for the LOB data, the LOBI stack utilizes the abstract representation to retrieve the requested LOB data from the LOB system in batches. In this manner, the batching mechanism utilized by the LOB system is abstracted from the client.

Retrieving data in batches from a line of business system

Decomposable ranking for efficient precomputing that selects preliminary ranking features comprising static ranking features and dynamic atom-isolated components

Cross domain data systems are verified by creating a program in the target domain language which can be used to determine for a result item whether the result item should be part of a query result. The program is then used in order to determine, for a set of target domain data corresponding to source domain data, a set of expected result items. These are compared with the result from the cross domain data system in order to provide verification of cross domain data systems. Multiple queries may be automatically generated and verified according to this technique.

Verification of cross domain data system query results

Systems and methods for a test harness that are provided that allow for effective control over both the data records and the test methods that are used in a software test run. Data records and/or test methods can be associated with a priority, such that a level of priority may be selected for a particular test run, and substantially only data records and test methods of the selected priority are used in the test. The invention may be implemented as a data driven test pattern class library. The data driven test pattern class library may generate concrete prioritized test cases dynamically using code document object model (code DOM) for substantially each data record by using a class decorated with known custom attributes. The invention can be used to help testers implement data driven tests effectively and in an easily maintainable fashion with minimal code.

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Bellevue, WA, United States of America

Karthik Kalyanaraman