Microsoft Technology Licensing, LLC

Other

Bo Thiesson

Christopher A Meek

David Earl Heckerman

David Maxwell Chickering

Bongshin Lee

Kenneth Ward Church

Eric Barber Vigesaa

Allan Folting

Tim Paek

Timothy S Paek

John Grant Bennett

Jack Wilson Stokes, Iii

Julian James Odell

Gary Voronel

Edward Wilkins Hardy

Xiang Wang

Jonathan Andreas Espenschied

Jue Wang

John Carlton Platt

Michael F Cohen

Robert L Rounthwaite

Yun-Cheng Ju

Carl Myers Kadie

Ying-Qing Xu

Fileno A Alleva

Timothy David Sharpe

Mei-Yuh Hwang

Robert J Ragno

John R Bennett

Chong Wang

D Maxwell Chickering

Oliver Scholz

Timothy Seung Yoon Paek

Edward Hart

Roland Franz Memisevic

Alex Bocharov

Jingu Kim

Olilver Scholz

Various technologies described herein pertain to detecting contextual anomalies in a behavioral network. Label propagation can be performed to construct contexts and assign respective context membership scores to users. Each context can be a respective subset of the users expected to have similar resource usages. The contexts can be constructed and the context membership scores can be assigned by combining behavioral information and contextual side information. The behavioral information can specify respective resource usages by the users within the behavioral network. Moreover, respective contextual anomaly scores for the users can be computed based on the respective context membership scores assigned to the users and the contextual side information. Further, the contextual anomalies can be detected from the contextual anomaly scores.

Detecting anomalies in behavioral network with contextual side information

Real-time query expansion (RTQE) is a process of supplementing an original query with additional terms or expansion choices that are ranked according to some figure of merit and presented while users are still formulating their queries. As disclosed herein, individual terms may be combined and submitted as a phrase into a query. By building the phase term-by-term, users can compositionally formulate queries while maintaining the same benefits that other RTQE interfaces offer. To promote greater flexibility in its working environment, the number of terms that are presented on a display may be reduced. In place of some terms, placeholders may be used and expanded by the user when necessary. This allows phrases to be readily presented on small displays (e.g., hand-held devices).

Term complete

Real-time query expansion (RTQE) is a process of supplementing an original query with addition terms or expansion choices that are ranked according to some figure of merit and presented while users are still formulating their queries. As disclosed herein, phrases may be presented and one or more terms of a focused-on phrase may be pinned (as desirable to the user). Subsequent lists may be presented as a function of pinned terms and/or user input. In one embodiment, a placeholder may be substituted for one or more pinned terms if less than some predetermined threshold of phrases is able to be presented based upon the pinned terms and/or user input, and another list of phrases may be presented as a function of a query using fewer than all the pinned terms. The placeholder may allow out-of-index phrases to be formed, for example, based upon two or more phrases and/or terms input by the user.

Phrase generation using part(s) of a suggested phrase

In one embodiment, an event impact signature detector may analyze a time series with external events. A data interfacemay receive a data setrepresenting the time series with external events. A processormay fit the data setinto a baseline time series model. The processormay iteratively determine each event locationfor multiple external eventsaffecting the baseline time series model. The processormay iteratively solve for each event impactof the multiple external eventsfactoring in interactions between the multiple external events

Detecting impact of extrinsic events on a time series

An augmented large index searching system and method for searching a database of items using a device having a limited input mechanism. Embodiments of the system and method present to a user in an augmented list view or a regular list view a list of items matching a sub-string search. The augmented list view contains a list of sub-group representations so that each sub-group is represented by an item in the sub-group most likely to be selected by the user. The user can select an item wanted by the user or refine the sub-string search by pinning a character to append the character to the sub-string and generate a revised sub-string. The above process is repeated using the revised sub-string. The list can be augmented by displaying visual features that indicate quantity and distinguish between items or characters by using coloring, highlighting, shading, size, and so forth.

Augmented list for searching large indexes

Described are variational Expectation Maximization (EM) embodiments for learning a mixture model using component-dependent data partitions, where the E-step is sub-linear in sample size while the algorithm still maintains provable convergence guarantees. Component-dependent data partitions into blocks of data items are constructed according to a hierarchical data structure comprised of nodes, where each node corresponds to one of the blocks and stores statistics computed from the data items in the corresponding block. A modified variational EM algorithm computes the mixture model from initial component-dependent data partitions and a variational R-step updates the partitions. This process is repeated until convergence. Component membership probabilities computed in the E-step are constrained such that all data items belonging to a particular block in a particular component-dependent partition behave in the same way. The E-step can therefore consider the blocks or chunks of data items via their representative statistics, rather than considering individual data items.

Variational EM algorithm for mixture modeling with component-dependent partitions

A mode-seeking clustering mechanism identifies clusters within a data set based on the location of individual data point according to modes in a kernel density estimate. For large-scale applications the clustering mechanism may utilize rough hierarchical kernel and data partitions in a computationally efficient manner. A variational approach to the clustering mechanism may take into account variational probabilities, which are restricted in certain ways according to hierarchical kernel and data partition trees, and the mechanism may store certain statistics within these trees in order to compute the variational probabilities in a computational efficient way. The clustering mechanism may use a two-step variational expectation and maximization algorithm and generalizations hereof, where the maximization step may be performed in different ways in order to accommodate different mode-seeking algorithms, such as the mean shift, mediod shift, and quick shift algorithms.

Variational mode seeking

Real-time query expansion (RTQE) is a process of supplementing an original query with additional terms or expansion choices that are ranked according to some figure of merit and presented while users are still formulating their queries. Individual terms may be combined and submitted as a phrase into a query. By building the phase term-by-term, users can compositionally formulate queries while maintaining the same benefits that other RTQE interfaces offer. The benefits include, reducing the number of keystrokes and improving retrieval performance. To promote greater flexibility in its working environment, the number of terms that are presented on a display may be reduced. In place of some terms, placeholders may be used and expanded by the user when necessary. This allows phrases to be readily presented on small displays (e.g., hand-held devices).

Phrase builder

A multi-modal search system (and corresponding methodology) that employs wildcards is provided. Wildcards can be employed in the search query either initiated by the user or inferred by the system. These wildcards can represent uncertainty conveyed by a user in a multi-modal search query input. In examples, the words 'something' or 'whatchamacallit' can be used to convey uncertainty and partial knowledge about portions of the query and to dynamically trigger wildcard generation.

Multi-modal search wildcards

Systems and methods are provided for parsimonious representation of large sets of multi-resolution value-item lists. A hierarchical data structure associated with the lists and conditioning variables is learnt while exploiting both semantics encoded in target variables and a notion of nearness among nodes at the same detail level in the hierarchical data structure. Such a level of description can be dictated by a depth in a tree data structure. A compression scheme that relies on (i) a similarity metric and (ii) recursive greedy pairing of value-item lists in order to promote elements populating a specific tree node upwards in the tree facilitates a parsimonious representation of the compressed hierarchic structure.

Parsimonious multi-resolution value-item lists

Systems and methods that create a classification of sentences in a language, and further construct associated local versions of language models, based on geographical location and/or other demographic criteria&#x2014;wherein such local language models can be of different levels of granularity according to chosen demographic criteria. The subject innovation employs a classification encoder component that forms a classification (e.g. a tree structure) of sentences, and a local language models encoder component, which employs the classification of sentences in order to construct the localized language models. A decoder component can subsequently enable local word wheeling and/or local web search by blending k-best answers from local language models of varying demographic granularity that match users demographics. Hence, k-best matches for input data by users in one demographic locality can be different from k-best matches for the same input by other users in another locality.

Demographic based classification for local word wheeling/web search

The claimed subject matter provides systems and/or methods that expand input data. An interface can obtain input data and a wildcard insertion component can modify the input data to include at least one implicit wildcard inserted at an end of each intended word. Additionally, an expansion component can generate a candidate list of expanded data based at least in part on the input data including the at least one implicit wildcard utilizing a language model that provides likely expansions of wildcards. Further, the expansion component can evaluate the input data at a server side.

Server side search with multi-word word wheeling and wildcard expansion

Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.

Bayesian approach for learning regression decision graph models and regression models for time series analysis

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Woodinville, WA, United States of America

Bo Thiesson