Abstract: In one embodiment, a method includes receiving, from a client system of a user of an online social network, a query inputted by the user, wherein the query comprises multiple n-grams; determining one or more head-terms and one or more modifier-terms of the n-grams based on a syntactic model; identifying one or more objects matching at least a portion of the query; ranking each identified object based on a quality of matching of the object to the determined head-terms and modifier-terms; and sending, to the client system in response to the query, a search-results interface for display, wherein the search-results interface includes one or more search results corresponding to one or more of the identified objects, respectively, each identified object corresponding to a search result having a rank greater than a threshold rank.

Abstract: A computer-implemented method, system, and computer program product for producing a semantic query by example are provided. The method includes receiving examples of potential results from querying a database table with an associated ontology, and extracting features from the database table and the examples based on the associated ontology. The method further includes training a classifier based on the examples and the extracted features, and applying the classifier to the database table to obtain a semantic query result. The method also includes outputting the semantic query result to a user interface, and requesting user feedback of satisfaction with the semantic query result. The method additionally includes updating the classifier and the semantic query result iteratively in response to the user feedback.

Abstract: In one embodiment, a method includes receiving a query including multiple n-grams; parsing the query to identify a subset of n-grams; generating, for each identified n-gram, an embedding of the n-gram; determining, for each identified n-gram, one or more word senses; calculating, for each word sense for each identified n-gram, a relatedness-score for the word sense based similarity metrics of the embedding of the word sense and the embeddings of each of the other word senses corresponding to the other identified n-grams; selecting, for each identified n-gram, one of the word senses determined for the identified n-gram having a highest relatedness-score; identifying objects matching at least a portion of the query; ranking each identified object based on a quality of matching of the object to selected word senses; and sending search results corresponding to one or more of the identified objects and having a rank greater than a threshold rank.

Abstract: In one embodiment, a method includes receiving, from a client system of a user of an online social network, a query inputted by the user, wherein the query comprises multiple n-grams; determining one or more head-terms and one or more modifier-terms of the n-grams based on a syntactic model; identifying one or more objects matching at least a portion of the query; ranking each identified object based on a quality of matching of the object to the determined head-terms and modifier-terms; and sending, to the client system in response to the query, a search-results interface for display, wherein the search-results interface includes one or more search results corresponding to one or more of the identified objects, respectively, each identified object corresponding to a search result having a rank greater than a threshold rank.

Abstract: In one embodiment, a method includes receiving a query including multiple n-grams; parsing the query to identify a subset of n-grams; generating, for each identified n-gram, an embedding of the n-gram; determining, for each identified n-gram, one or more word senses; calculating, for each word sense for each identified n-gram, a relatedness-score for the word sense based similarity metrics of the embedding of the word sense and the embeddings of each of the other word senses corresponding to the other identified n-grams; selecting, for each identified n-gram, one of the word senses determined for the identified n-gram having a highest relatedness-score; identifying objects matching at least a portion of the query; ranking each identified object based on a quality of matching of the object to selected word senses; and sending search results corresponding to one or more of the identified objects and having a rank greater than a threshold rank.

Abstract: Video capture devices may be held in a frame having at least two openings and a plurality of slots on a first side and a plurality of tabs on a second side opposite the first side, the slots and tabs positioned such that when the frame is folded by folding the first side toward the second side, the tabs may be folded toward the first side, inserted into the slots and then folded parallel to the first side to secure the frame in a closed position. An application on a first device in the frame may establish a connection with a second device in the frame, synchronize a clock in the first device with a clock in the second device, initiating simultaneous video capture, record data indicating changes in orientation of the devices, and transmit the captured videos and the data indicating changes in orientation to a video processing tool.

Abstract: A system, method or computer readable storage device to derive a taxonomy from keywords is described herein. A domain-dependent taxonomy from a set of keywords may be automatically derived by leveraging both a general knowledgebase and keyword search. For example, concepts may be deduced with the technique of conceptualization, and context information may be extracted from a search engine. Then, the taxonomy may be constructed using a tree algorithm.

Abstract: Video capture devices may be held in a frame having at least two openings and a plurality of slots on a first side and a plurality of tabs on a second side opposite the first side, the slots and tabs positioned such that when the frame is folded by folding the first side toward the second side, the tabs may be folded toward the first side, inserted into the slots and then folded parallel to the first side to secure the frame in a closed position. An application on a first device in the frame may establish a connection with a second device in the frame, synchronize a clock in the first device with a clock in the second device, initiating simultaneous video capture, record data indicating changes in orientation of the devices, and transmit the captured videos and the data indicating changes in orientation to a video processing tool.

Abstract: Embodiments are directed to processing queries using schema graph traversal and to establishing a schema graph that allows queries to be answered by traversing graph nodes. In one scenario, a computer system receives a query which specifies relational tables and corresponding relationships that are to be retrieved from a relational database. The computer system accesses a schema graph that includes graph nodes representing relational tables, as well as edges that identify relationships between the relational tables. The graph nodes include relational data that was loaded from one storage area (e.g. a non-volatile storage area), and the schema graph is stored in a second storage area (e.g. a volatile storage area). The computer system then traverses the schema graph, beginning at a set of graph nodes and continuing along the edges to other graph nodes until the query has been satisfied, and then reports the results of the graph traversal.

Abstract: Embodiments are directed to processing queries using schema graph traversal and to establishing a schema graph that allows queries to be answered by traversing graph nodes. In one scenario, a computer system receives a query which specifies relational tables and corresponding relationships that are to be retrieved from a relational database. The computer system accesses a schema graph that includes graph nodes representing relational tables, as well as edges that identify relationships between the relational tables. The graph nodes include relational data that was loaded from one storage area (e.g. a non-volatile storage area), and the schema graph is stored in a second storage area (e.g. a volatile storage area). The computer system then traverses the schema graph, beginning at a set of graph nodes and continuing along the edges to other graph nodes until the query has been satisfied, and then reports the results of the graph traversal.

Abstract: The present invention provides an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure in which each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence because each event is associated with a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed herein enables the efficient retrieval from the database of all subsequences (contiguous and non-contiguous) that match a given query sequence both by events and by weights. The index structure also takes into consideration the nonuniform frequency distribution of events in the sequence data.

Abstract: A system, method or computer readable storage device to derive a taxonomy from keywords is described herein. A domain-dependent taxonomy from a set of keywords may be automatically derived by leveraging both a general knowledgebase and keyword search. For example, concepts may be deduced with the technique of conceptualization, and context information may be extracted from a search engine. Then, the taxonomy may be constructed using a tree algorithm.

Abstract: Disclosed are a method, information processing system, and computer readable medium for processing queries. The method includes receiving a data query for a set of hierarchical markup documents. At least one query path expression is extracted from the data query. The query path is processed against at least one shared hierarchical markup document in a plurality of shared hierarchical markup documents. The plurality of shared hierarchical documents is associated with the set of hierarchical markup documents. In response to the shared hierarchical markup document completely matching the query path expression, a query result for the data query is generated. The query result is based on the processing of the query path expression against at least one of the shared hierarchical markup document and the difference hierarchical markup document.

Abstract: Disclosed in a method and structure for searching data in databases using an ensemble of models. First the invention performs training. This training orders models within the ensemble in order of prediction accuracy and joins different numbers of models together to form sub-ensembles. The models are joined together in the sub-ensemble in the order of prediction accuracy. Next in the training process, the invention calculates confidence values of each of the sub-ensembles. The confidence is a measure of how closely results form the sub-ensemble will match results from the ensemble. The size of each of the sub-ensembles is variable depending upon the level of confidence, while, to the contrary, the size of the ensemble is fixed. After the training, the invention can make a prediction. First, the invention selects a sub-ensemble that meets a given level of confidence.

Abstract: Disclosed are a method, information processing system, and a computer readable medium for managing documents. The method includes analyzing a plurality of hierarchical markup documents, wherein each hierarchical markup document is representable by a hierarchical tree structure. A shared hierarchical markup document associated with the plurality of hierarchical markup documents is generated based on the analyzing. Each hierarchical markup document in the plurality of hierarchical markup documents is compared with the shared hierarchical document. A plurality of difference hierarchical markup documents is generated based on the comparing.

Abstract: Load shedding schemes for mining data streams. A scoring function is used to rank the importance of stream elements, and those elements with high importance are investigated. In the context of not knowing the exact feature values of a data stream, the use of a Markov model is proposed herein for predicting the feature distribution of a data stream. Based on the predicted feature distribution, one can make classification decisions to maximize the expected benefits. In addition, there is proposed herein the employment of a quality of decision (QoD) metric to measure the level of uncertainty in decisions and to guide load shedding. A load shedding scheme such as presented herein assigns available resources to multiple data streams to maximize the quality of classification decisions. Furthermore, such a load shedding scheme is able to learn and adapt to changing data characteristics in the data streams.

Abstract: A computer-implemented method, system, and computer program product for producing a semantic query by example are provided. The method includes receiving examples of potential results from querying a database table with an associated ontology, and extracting features from the database table and the examples based on the associated ontology. The method further includes training a classifier based on the examples and the extracted features, and applying the classifier to the database table to obtain a semantic query result. The method also includes outputting the semantic query result to a user interface, and requesting user feedback of satisfaction with the semantic query result. The method additionally includes updating the classifier and the semantic query result iteratively in response to the user feedback.

Abstract: A method (and structure) for processing an inductive learning model for a dataset of examples, includes dividing the dataset of examples into a plurality of subsets of data and generating, using a processor on a computer, a learning model using examples of a first subset of data of the plurality of subsets of data. The learning model being generated for the first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of the dataset, the ensemble model thereby providing an evolving estimated learning model for the entirety of the dataset if all the subsets were to be processed. The generating of the learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of the ensemble model.

Abstract: A computer implemented method, apparatus, and computer usable program code for processing multi-way stream correlations. Stream data are received for correlation. A task is formed for continuously partitioning a multi-way stream correlation workload into smaller workload pieces. Each of the smaller workload pieces may be processed by a single host. The stream data are sent to different hosts for correlation processing.

Abstract: The present invention provides a ViST (or “virtual suffix tree”), which is a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, it is shown that querying XML data is equivalent to finding (non-contiguous) subsequence matches. A variety of XML queries, including those with branches, or wild-cards (‘*’ and ‘//’), can be expressed by structure-encoded sequences. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure.