Patent application number | Description | Published |
20110295783 | Multiple Domain Anomaly Detection System and Method Using Fusion Rule and Visualization - The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules. | 12-01-2011 |
20120041901 | System and Method for Knowledge Pattern Search from Networked Agents - One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research. | 02-16-2012 |
20120143800 | Method and System for Knowledge Pattern Search and Analysis for Selecting Microorganisms Based on Desired Metabolic Property or Biological Behavior - Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater. | 06-07-2012 |
20140088883 | Method and System for Knowledge Pattern Search and Analysis for Selecting Microorganisms Based on Desired Metabolic Property or Biological Behavior - Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater. | 03-27-2014 |
Patent application number | Description | Published |
20130238669 | Using Target Columns in Data Transformation - A data transform leverages a known hierarchy within a target data structure, in order to improve query and mapping capabilities and enhance performance. Where a target data structure is hierarchical, output data of that target data structure is often built in the document order of the nodes in the structure (from top down and from left to right). Hence, when the data for a child node in the target structure is being built, the data for the parent nodes of the child node has been built. Embodiments utilize this available portion of the target data in the form of target columns, to increase processing efficiency of the transformation process. Use of target columns according to embodiments may also allow powerful and concise expression of mapping logic in the transform, facilitating the use of functions such as selection (e.g. Where clauses), uniqueness (e.g. DISTINCT), ordering (Order By, Group By), and Aggregation. | 09-12-2013 |
20130262417 | Graphical Representation and Automatic Generation of Iteration Rule - Embodiments relate to graphical representation and/or automatic generation of an iteration rule in mapping design that is to integrate or transform one or more input data sets into another target data set. The input and output data set can be of flat or hierarchical in nature. In an embodiment, a graphical interface allows users to specify an iteration rule (e.g. JOIN operation in a relational database) in a tree-like structure (e.g. a JOIN tree). The interface allows users to visualize and implement complicated and powerful combinations of multiple data sets, including data sets exhibiting hierarchical structure. Drag-and-drop techniques may be employed to reduce the need for manual typing. Also disclosed are procedures automatically generating an iteration rule based on the data mapping information, thereby reducing a need for manual mapping. | 10-03-2013 |
20130282740 | System and Method of Querying Data - A system and method of querying data. The method includes transforming first data according to a unified data model. The unified data model has a hierarchical structure with tree nodes and leaf nodes. A leaf node contains a table. The method further includes executing a unified data model query on the first data (having been transformed) to result in second data. The method further includes outputting the second data. | 10-24-2013 |
20140201244 | METHOD FOR REPRESENTING AND STORING HIERARCHICAL DATA IN A COLUMNAR FORMAT - A computer implemented system, program product, and method that organizes hierarchical data into a plurality of columns is disclosed. A schema interface is defined for the data and two types of columns, value columns and occurrence columns, are used. Each value column stores the values for a field. Each occurrence column stores the occurrence numbers for a node that is repeatable, or optional, or in a choice group. The hierarchical relationship of the data is jointly preserved by the schema interface and the occurrence numbers in the occurrence columns. A database management system built upon the method is capable of operating at higher efficiency by combining some of the best aspects of relational database and hierarchical database management systems. The computer implemented method also provides a new and efficient method of exchanging data over networks. | 07-17-2014 |
Patent application number | Description | Published |
20100011031 | STORING LOG DATA EFFICIENTLY WHILE SUPPORTING QUERYING - A logging system includes an event receiver and a storage manager. The receiver receives log data, processes it, and outputs a column-based data “chunk.” The manager receives and stores chunks. The receiver includes buffers that store events and a metadata structure that stores metadata about the contents of the buffers. Each buffer is associated with a particular event field and includes values from that field from one or more events. The metadata includes, for each “field of interest,” a minimum value and a maximum value that reflect the range of values of that field over all of the events in the buffers. A chunk is generated for each buffer and includes the metadata structure and a compressed version of the buffer contents. The metadata structure acts as a search index when querying event data. The logging system can be used in conjunction with a security information/event management (SIEM) system. | 01-14-2010 |
20130073573 | QUERY PIPELINE - A query pipeline is created ( | 03-21-2013 |
20140195502 | MULTIDIMENSION COLUMN-BASED PARTITIONING AND STORAGE - A data storage system includes a storage engine to partition data across multiple dimensions. The storage engine determines chunks according to the partitioning, and performs column-based storage of the chunks. | 07-10-2014 |
20140244650 | DISTRIBUTED EVENT PROCESSING - A distributed event processing method includes providing a plurality of connectors. Each provided connector is configured to acquire event data from an assigned data source, partition acquired event data into clusters, and divide each cluster into chunks. The method also includes collecting the chunks from the plurality of connectors and storing the chunks to a data file that can be queried. | 08-28-2014 |
20140280075 | MULTIDIMENSION CLUSTERS FOR DATA PARTITIONING - A data storage system includes a partitioning module to partition data across multiple dimensions simultaneously. The partitioning may be based on a sizing parameter for each dimension. The partitioning module stores a cluster including the partitioned event data and metadata including attributes identifying the cluster. | 09-18-2014 |
Patent application number | Description | Published |
20090037316 | Allocating Goods to Bidders in Combinatorial Auctions - Embodiments are directed to systems, methods, and apparatus for allocating goods to bidders in combinatorial auctions. In one embodiment, bids are received in a combinatorial auction and the winner determination problem is modeled as an interval knapsack problem (I-KP) or an interval multiple-choice knapsack problem (I-MCKP), efficient algorithms (both pseudo-polynomial-time exact algorithms and FPTAS) for I-KP (and I-MCKP) are used to compute an allocation of goods to winning bidders. | 02-05-2009 |
20090037317 | Bidding in Online Auctions - Embodiments are directed to systems, methods, and apparatus for bidding in online auctions. In one embodiment, bids for advertising include an amount that is a function of an expected value-per-click and a fraction of a budget already spent for advertising slots. | 02-05-2009 |
20100082433 | Using A Threshold Function For Bidding In Online Auctions - One embodiment is a method that generates bids at an online search auction. The method uses a threshold function to decide which slot to obtain and bids accordingly. | 04-01-2010 |
20100100516 | Predicting User-Item Ratings - A method of predicting user-item ratings includes providing a first matrix of hidden variables associated with individual items, a second matrix of hidden variables associated with individual users, a third matrix of predicted user-item ratings derived from an inner product of vectors in the first and second matrices, and a fourth matrix of actual user-item ratings. The first and second matrices are alternately fixed and solved with a weighted-λ regularization of at least one of the first and second matrices by minimizing a sum of squared errors between actual user-item ratings in the fourth matrix and corresponding predicted user-item ratings in the third matrix repeatedly until a stopping criterion is satisfied. | 04-22-2010 |