Abstract: Identifiers of items generated in response to a query are each ranked in a way that considers the other identified items. Topologies are generated that correspond to features of the identified items. Each topology may be a Markov chain that includes a node for each identified item and directed edges between the nodes. Each directed edge between a node pair has an associated transition probability that represents the likelihood that a hypothetical user would change their preference from a first node in the pair to the second node in the pair when considering the feature associated with the topology. The topologies are weighted according to the relative importance of the features that correspond to the topologies. The weighted topologies are used to generate a stationary distribution of the identified items, and the identified items are ranked using the stationary distribution.

Abstract: Training data is provided for tail queries based on a phenomena in search engine user behavior—referred to herein as “domain trust”—as an indication of user preferences for individual URLs in search results returned by a search engine for tail queries. Also disclosed are methods for generating training data in a search engine by forming a collection of query+URL pairs, identifying domains in the collection, and labeling each domain. Other implementations are directed ranking search results generated by a search engine by measuring domain trust for each domain corresponding to each URL from among a plurality of URLs and then ranking each URL by its measured domain trust.

Abstract: Techniques and systems are disclosed for returning temporally-aware results from an Internet-based search query. To determine if a query is temporally-based one or more query features are collected and input into a trained classifier, yielding a temporal classification for the query. Further, if a query is classified as temporal, the query results are shifted by determining an alternate set of results for the query, and returning one or more alternate results to one or more users. Based on user interactions with the one or more alternate results, the classifier can be updated, for example, by changing the query to a non-temporal query if the user interactions identify it as such.

Abstract: A set of queries, such as a search log, is divided into commercial queries and non-commercial queries. A first set of query communities is determined from the non-commercial queries and a second set is determined from the commercial queries. The query communities are correlated based on the users who submitted the queries and instances where a query from the first set of query communities was followed by a query from the second set to generate a mapping between the first set of query communities and the second set. Later, a non-commercial query is received from a user, and the mapping is used to predict one or more commercial queries that the user is likely to submit in the future based on the non-commercial query. One or more of the commercial queries are presented to the user according to the mapping with search results responsive to the non-commercial query.

Abstract: Described is a search (e.g., web search) technology in which concepts are returned in response to a query in addition to (or instead of) search results in the form of traditional links. Each concept generally corresponds to a set of links to content that are more directed towards a possible user intention, or information need, with respect to that query. If a user selects a concept, that concept's links are exposed to facilitate selection of a document the user finds relevant. In this manner, much more than the top ten ranked links may be provided for a query, each set of other links arranged by the concepts. Also described is processing a query log or other data store to optionally find related queries and find the concepts, e.g., by clustering a relationship graph built from the query log to find dense subgraphs representative of the concepts.

Abstract: Described is releasing output data representing a search log, in which the data is suitable for most data mining/analysis applications, but is safe to publish by preserving user privacy. The search log is processed such that a query is only included if a sufficient count of that query is present; noise may be added. User contributions that are considered may be limited to a maximum number of queries. The output may indicate how often (possibly plus noise) that each query appeared. Other output may comprise a query-action graph, a query-inaction graph and/or a query-reformulation graph, with nodes representing queries and nodes representing actions, inactions or reformulations (e.g., clicked URLs, skipped URLs, or selected related queries), and edges between nodes representing action, skip or selection counts (possibly plus noise). The output may correspond to the top results/related queries returned from a search.

Abstract: A method for discovery of a cluster of objects in an arbitrary undirected graph. A subset of the objects is determined by performing a random walk starting from a first object of the objects and following a plurality of random edges of subsequent objects, the subset comprising the first object and the subsequent objects. An enlarged subset is determined by enlarging the subset to include other objects well-connected to the subset. It is determined whether the enlarged subset is a cluster.

Abstract: Techniques and systems are disclosed for returning temporally-aware results from an Internet-based search query. To determine if a query is temporally-based one or more query features are collected and input into a trained classifier, yielding a temporal classification for the query. Further, if a query is classified as temporal, the query results are shifted by determining an alternate set of results for the query, and returning one or more alternate results to one or more users. Based on user interactions with the one or more alternate results, the classifier can be updated, for example, by changing the query to a non-temporal query if the user interactions identify it as such.

Abstract: A method and system for finding a conjunctive cluster is disclosed. The method includes identifying a first group of points, identifying a second group of points, drawing a first sample from the first group of points of a first predetermined size and identifying subsets of the first sample. For each subset of the first sample of a second predetermined size the method includes identifying a subgroup of the second group of points that share an intersection with all of the points in the first sample of points, identifying a sub group of said first group of points that share an intersection with a specified number of the points in the subgroup of the second group of points and outputting a third group of points. It should be appreciated that the product of the magnitude of the subgroup of the second group and the magnitude of the subgroup of the first group is maximized.

Abstract: A method for predicting parts for onsite repair which takes into account a repair history and the costs associated with mis-predictions. Parts for onsite repair of a product are predicted by determining an expected waste for one or more parts of the product. The parts having a lowest expected waste are selected and sent to the onsite repair. The expected waste indicates parts that are responsible for high support costs and highlights the mistakes being made and scores the mistakes by actual cost.

Abstract: Data from a click log may be used to generate training data for a search engine. The pages clicked as well as the pages skipped by a user may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query.

Abstract: The present invention is a system or method for assisting in the maintenance and servicing of computers. The system, for example, comprises a cluster database containing information relating to one or more computer clusters, the information relating to each cluster comprising one or more cluster descriptions and one or more representations of configuration information values that characterize typical computer members of the cluster, and also a tracker database containing configuration information values gathered from and relating to one or more computers.

Abstract: A method of global data placement. The method includes assigning one or more workloads to one or more compute servers such that each workload flows to one compute server, assigning the data chunks that the workloads accesses to one or more storage servers, and determining how the workloads access the data.

Abstract: A method and system for identifying clusters of similarly-configured computers. The method, for example, comprises gathering system and business configuration information values from a set of computers. Next, at least a portion of the system and business configuration information values gathered from the set of computers is analyzed along with the numbers of computers that are configured in various ways. Then, using the result of the analysis as a guide, logical expressions of configuration information values are selected for use as definitions of one or more clusters of similarly-configured computers, the selection process generally maximizing the number of computers included within each cluster and also generally maximizing the number of configuration information value specifications included within the definition of each cluster.

Abstract: A system and method is disclosed for determining a value of an element, having a k-th rank. The method discloses: calculating a total number of elements in a first and second dataset; prohibiting parties from accessing each other's dataset; ranking the elements within each dataset; computing a total number of elements in the datasets each having a value less than a test value; computing a total number of elements in the datasets each having a value greater than the test value; and setting the value of the element, having the k-th rank, equal to the test value, if the total number of elements having values less than the test value is ? the k-th rank minus one, and the total number of elements having values greater than the test value is ? the total number of elements minus the k-th rank. The system discloses means for practicing the method.

Abstract: A method and system for identifying clusters of similarly-configured computers. The method, for example, comprises gathering system and business configuration information values from a set of computers. Next, at least a portion of the system and business configuration information values gathered from the set of computers is analyzed along with the numbers of computers that are configured in various ways. Then, using the result of the analysis as a guide, logical expressions of configuration information values are selected for use as definitions of one or more clusters of similarly-configured computers, the selection process generally maximizing the number of computers included within each cluster and also generally maximizing the number of configuration information value specifications included within the definition of each cluster.

Abstract: The present invention is a system or method for assisting in the maintenance and servicing of computers. The system, for example, comprises a cluster database containing information relating to one or more computer clusters, the information relating to each cluster comprising one or more cluster descriptions and one or more representations of configuration information values that characterize typical computer members of the cluster, and also a tracker database containing configuration information values gathered from and relating to one or more computers.

Abstract: (1) An approximate center-based clustering that utilizes sampling to cluster a set of n points to identify k>0 centers with quality assurance, but without the drawbacks of sample size and running time dependence on n. (2) An approximate conceptual clustering algorithm that utilizes sampling to identify k disjoint conjunctions with novel quality assurance, also without the drawbacks of sample size and running time dependence on n.

Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , Sp; 2) for each piece Si, determining a set Di of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set Di by the number of points in the corresponding piece Si assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

Abstract: A method and system for finding a conjunctive cluster is disclosed. The method includes identifying a first group of points, identifying a second group of points, drawing a first sample from the first group of points of a first predetermined size and identifying subsets of the first sample. For each subset of the first sample of a second predetermined size the method includes identifying a subgroup of the second group of points that share an intersection with all of the points in the first sample of points, identifying a sub group of said first group of points that share an intersection with a specified number of the points in the subgroup of the second group of points and outputting a third group of points. It should be appreciated that the product of the magnitude of the subgroup of the second group and the magnitude of the subgroup of the first group is maximized.