Patent application number | Description | Published |
20100094852 | SCHEDULING QUERIES USING A STRETCH METRIC - A query scheduler orders queries in a queue. Each query is executed based on its position in the queue. When a new query is received, the new query is inserted in the queue. A position in the queue for inserting the new query is determined based on a stretch metric for each query in the queue. | 04-15-2010 |
20100095299 | MIXED WORKLOAD SCHEDULER - A mixed workload scheduler and operating method efficiently handle diverse queries ranging from short less-intensive queries to long resource-intensive queries. A scheduler is configured for scheduling mixed workloads and comprises an analyzer and a schedule controller. The analyzer detects execution time and wait time of a plurality of queries and balances average stretch and maximum stretch of scheduled queries wherein query stretch is defined as a ratio of a sum of wait time and execution time to execution time of a query. The schedule controller modifies scheduling of queries according to service level differentiation. | 04-15-2010 |
20100198758 | DATA CLASSIFICATION METHOD FOR UNKNOWN CLASSES - A system and method for creating a CD Tree for data having unknown classes are provided. Such a method can include dividing training data into a plurality of subsets of node training data at a plurality of nodes arranged in a hierarchical arrangement, wherein the node training data has a range. Furthermore, dividing node training data at each node can include, ordering the node training data, generating a plurality of separation points and a plurality of pairs of bins from the node training data, wherein each pair of bins includes a first bin and a second bin with a separation point being located between the first bin and the second bin, and classifying the node training data into either the first bin or the second bin for each of the separation points, wherein the classifying is based on a data classifier. Validation data can be utilized to calculate the bin accuracy between the node training data bin pairs and the validation data bin pairs for each separation point, and the separation point having a high bin accuracy can be selected as the node separation point. | 08-05-2010 |
20100262613 | Data Stream Processing - A method of processing a stream of raw data from a plurality of distributed data producing devices includes reducing the raw data to a plurality of representative synopsis coefficients, organizing the synopsis coefficients into a data structure with at least three dimensions, including a time window dimension and an accuracy dimension. Responsive to a detected anomaly in the data structure, at least one of a predetermined autonomous action and an action directed by a user is performed. | 10-14-2010 |
20100280857 | MODELING MULTI-DIMENSIONAL SEQUENCE DATA OVER STREAMS - One embodiment is a method that builds a model of multi-dimensional sequence data in real-time with cuboids that aggregate the multi-dimensional sequence data over both patterns and dimensions. The model provides search results for a query. | 11-04-2010 |
20110010405 | Compression of non-dyadic sensor data organized within a non-dyadic hierarchy - Sensor data is received from one or more sensors. The sensor data is organized within a hierarchy. The sensor data is organized within a hierarchy that is non-dyadic. A processor of a computing device generates a discrete wavelet transform, based on the sensor data and based on the hierarchy of the sensor data, to compress the sensor data. The sensor data, as has been compressed via generation of the discrete wavelet transform, is processed. | 01-13-2011 |
20110113009 | Outlier data point detection - New data points are added to a streaming window of data points and existing data points are removed from the window over time. Each data point has a value for each of one or more dimensions. Each time a given new data point is added to the window or a given existing data point is removed from the window, one or more outlier detection data structures are updated. Each outlier detection data structure encompasses the data points within the streaming window for a corresponding dimension. The outlier detection data structures are used to detect outlier data points within the window over selected one or more dimensions. | 05-12-2011 |
20110191324 | TRANSFORMATION OF DIRECTED ACYCLIC GRAPH QUERY PLANS TO LINEAR QUERY PLANS - Methods, computer-readable storage media and computer systems are provided for transforming a directed acyclic graph (“DAG”) query plan into a linear plan. A DAG query plan may include a first operator and a second operator that are scheduled to be executed in parallel. The DAG query plan may be modified so that the first and second operators are executed in series as an upstream operator and a downstream operator. A data unit output from the upstream operator may be marked to indicate that the data unit has been processed by the upstream operator. The data unit received as input at the downstream operator may be inspected to determine whether the data unit has been marked. Once in linear form, the query plan may be optimized to conserve computing resources. | 08-04-2011 |
20120041963 | PROVIDING SELECTED ATTRIBUTES OF STREAMING DATA FOR DISPLAY BY A VISUALIZATION ENGINE - An adapter receives tuples of streaming data from a streaming data source. The adapter extracts selected attributes from the tuples of streaming data, and writes the selected attributes to a buffer associated with a visualization engine for displaying the selected attributes in a visualization screen. The selected attributes are written to the buffer according to a predefined format supported by the visualization engine. The adapter receives interactive user input to change the selected attributes to be extracted from the tuples and written to the buffer. | 02-16-2012 |
20120072390 | DETERMINING WHETHER A POINT IN A DATA STREAM IS AN OUTLIER USING HIERARCHICAL TREES - A technique that includes receiving a data stream that is indicative of a plurality of multi-dimensional points in a processor-based machine and for each dimension, organizing data indicative of values of the points in the dimension in an associated hierarchical tree. The technique includes using the processor-based machine to determine whether a given point of the plurality of points is an outlier based on a combination of the trees. | 03-22-2012 |
20120078903 | IDENTIFYING CORRELATED OPERATION MANAGEMENT EVENTS - A technique includes receiving data indicative of operation management events, where each event occurs at an associated time. The technique includes processing the data to selectively group the events in episodes based on the associated times and identifying which events are correlated based at least in part on the episodes. | 03-29-2012 |
20120078912 | METHOD AND SYSTEM FOR EVENT CORRELATION - A method for event correlation includes receiving events from a network of systems and classifying the events into itemsets, where each itemset includes a set of frequently correlated events. The method also includes calculating a confidence value for each of the itemsets, identifying itemsets whose confidence values conform to a confidence criterion, and varying the confidence criterion to reduce the number of the identified itemsets. A computer program product and data processing system are also disclosed. | 03-29-2012 |
20120197950 | SENTIMENT CUBE - A sentiment cube system is disclosed. In one example, the system discloses a sentiment storage, including a sentiment cube data structure having a set of cells arranged by a set of dimensions. The system includes a computer programmed with executable instructions which operate a set of modules, wherein the modules comprise: a sentiment storage module which receives sentiment values associated with a set of entity features, and then populates a hierarchy of the cells in the sentiment cube with the sentiment values. A sentiment analysis module effecting a set of operations on the sentiment cube. | 08-02-2012 |
20120226652 | Event prediction - A selected set of one or more first events that have occurred within current data is received. An episode set of which the selected set is a subset, the episode set including one or more second events that have occurred within historical data related to the current data is identified. One or more third events that occurred within the historical data within a predetermined time horizon after the one or more second events of the episode set occurred within the historical data are identified. The one or more third events are predicted to likely occur within the current data as a result of the one or more first events having occurred within the current data. | 09-06-2012 |