Abstract: A system and method for efficiently removing duplicate data blocks at a fine-granularity from a storage array. A data storage subsystem supports multiple deduplication tables. Table entries in one deduplication table have the highest associated probability of being deduplicated. Table entries may move from one deduplication table to another as the probabilities change. Additionally, a table entry may be evicted from all deduplication tables if a corresponding estimated probability falls below a given threshold. The probabilities are based on attributes associated with a data component and attributes associated with a virtual address corresponding to a received storage access request. A strategy for searches of the multiple deduplication tables may also be determined by the attributes associated with a given storage access request.

Abstract: A data storage device is disclosed including a non-volatile media having a first and a second plurality of physical locations, the first plurality of physical locations storing user data and the non-volatile media storing first metadata associating each of the first plurality of physical locations with a logical block address (LBA), and a mapping table including a mapping of each LBA to a current physical location. The data storage device further includes control circuitry that reads the first metadata to obtain a first plurality of LBAs, reads the mapping table to determine a current physical location for the first plurality of LBAs, and compares the current physical location of each of the first plurality of LBAs to a corresponding one of the first plurality of physical locations to identify valid user data in the first plurality of physical locations for migration into the second plurality of physical locations.

Abstract: A hybrid database table is stored as both a row and a column store. One or more techniques may be employed alone or in combination to enhance performance of the hybrid table by regulating access to, and/or the size of, the processing-intensive column store data. For example during an insert operation, the column store data may be searched for a uniqueness violation only after certain filtering and/or boundary conditions have been considered. In another technique, a hybrid table manager may control movement of data to the column store based upon considerations such as frequency of access, or underlying business logic. In still another technique, querying of the hybrid table may result in a search of the column store data only after an initial search of row store data fails to return a result.

Abstract: Table detection is always an important task of document analysis and recognition. In this paper, we propose a novel and effective table detection method via visual separators and geometric content layout information, targeting at PDF documents. The visual separators refer to not only the graphic ruling lines but also the white spaces to handle tables with or without ruling lines. Furthermore, we detect page columns in order to assist table region delimitation in complex layout pages. Evaluations of our algorithm on an e-Book dataset and a scientific document dataset show competitive performance. It is noteworthy that the proposed method has been successfully incorporated into a commercial software package for large-scale Chinese e-Book production.

Abstract: Techniques are described in which at least one column is added to a table in a column-based database that has a plurality of columns and table fields. A shadow-in-place upgrade of the column-based database can be performed that adds at least one new column to the database. Thereafter, access to the column-based database can be seamlessly enabled before, during, and after the upgrade through a plurality of views. The views define a subset of the fields in the database that are visible. Related apparatus, systems, techniques and articles are also described.

Abstract: A database table within a database to persist within a cache as a cached table can be identified. The database can be a relational database management system (RDBMS) or an object oriented database management system (OODBMS). The cache can be a database cache. Database transactions within can be logged within a log table and the cached table within the cache can be flagged as not cached during runtime. An asynchronous replication of the database table to the cached table can be performed. The replication can execute the database transactions within the log table upon the cached table. The cached table can be flagged as cached when the replication is completed.

Abstract: A system for computing an optimal deployment of at least one web application in a multi-datacenter system comprising a collector for collecting performance measurements with regard to a web application executed in the multi-datacenter system and grouping the performance measurements according to locations of a plurality of clients accessing the web application; a data repository for maintaining at least a performance table including at least the performance measurements grouped according to the plurality of client locations and a service level agreement (SLA) guaranteed to clients in the plurality of client locations; and an analyzer for processing at least information stored in the performance table for generating a recommendation on an optimal deployment of the web application in at least one combination of datacenters in the multi-datacenter system by computing an expected SLA that can be guaranteed to the clients in each combination of datacenters.

Abstract: A method may include receiving a reconfiguration to a first Virtual Local Area Network (VLAN)/spanning tree table, where the first VLAN/spanning tree table has a first identifier and is associated with a region of a network; updating the first VLAN/spanning tree table to generate a second VLAN/spanning tree table based on the reconfiguration; determining a second identifier of the second VLAN/spanning tree table; and generating a list of identifiers associated with the region of the network, the list including the first identifier and the second identifier.

Abstract: A technique for composing a virtual desktop associated with one or more applications in a virtualized computing environment. The technique includes generating file system level metadata in the desktop image for applications that create a perception that data blocks in the desktop image have been allocated to applications, but not actually copying any of the application data into the allocated blocks. Instead, the technique builds a mapping table between disk block numbers and the application data, which can be stored in a separate application store. The disclosed techniques provide a more efficient way to compose a virtual desktop, compared to current virtual desktop adoption techniques.

Abstract: A management application refers to an application management table, and acquires the operation states of all VMs included in an additional application of which the priority related to the execution of a job that has requested from the image forming apparatus. The management application detects the additional application including only VM that is not executing processing based on the operation states of the acquired VMs, and deletes the VM included in the detected additional application.

Abstract: An exemplary embodiment of a multi-tenant database system is provided. The system includes a multi-tenant database, an entity definition table, and a data processing engine. The database has database objects for multiple tenants, including an existing object for a designated tenant. Each entry in the existing object has a respective entity identifier. The definition table has metadata entries for the database objects, including a metadata entry for the existing object. This metadata entry has a tenant identifier for the designated tenant, an entity name for the existing object, and an old key prefix for the existing object. Each entity identifier of the existing object begins with the old key prefix. The engine performs a data truncation operation on the existing object by updating the metadata entry to replace the old key prefix with a new key prefix. This results in an updated object that is identified by the new key prefix and the tenant identifier.

Abstract: A method of automatically extracting data from an electronic document including tables is provided. The method includes: automatically identifying rows of the table using gaps in horizontal projections of the plurality of image sections, wherein at least some of the identified rows in close proximity are collected to form table formations; and automatically identifying columns of the table using at least some of the plurality of image sections that are vertically aligned, wherein the identified columns are grown in each of the table formations using gaps in vertical projections of the plurality of image sections until an obstruction is reached. The method further includes automatically identifying labels in the plurality of corresponding image sections to associate the identified labels with at least one of the identified columns and the identified rows; and automatically extracting data from cells of the table formed by the identified rows and columns.

Abstract: A database management system suitable for implementation in a cloud computing environment. Operational nodes are assigned as controller-nodes, compute-nodes or storage-nodes. Groups of controller-nodes form controller nodegroups, and groups of compute-nodes forming compute nodegroups, and groups of storage nodes forming storage nodegroups. The number of operational nodes, and their available assignment as compute-nodes or storage-nodes vary during execution of the queries. The queries also specify one or more tables for an associated database operation, with each such table being assigned to a respective storage nodegroup. In addition, the number of operational nodes executing a given query may change a given time interval, by (a) changing the compute-nodegroup associated with a connection, or (b) adding or removing nodes from the compute nodegroup associated with a connection; and/or distributing data from the tables among the nodes in the storage nodegroup to which the table is assigned based on a Distribution Method which may be either data dependent or data independent. A controller node further executes a Dynamic Query Planner (DQP) process that develops a query plan.

Abstract: This paper presents the design, implementation, and evaluation of an interactive tabletop system that supports co-located meeting capture and asynchronous search and review of past meetings. The goal of the project is to evaluate the design of a conference table that augments the everyday work patterns of small collaborative groups by incorporating an integrated annotation system. We present a holistic design that values hardware ergonomics, supports heterogeneous input modalities, generates a memory of all user interactions, and provides access to historical data on and off the table. We present a user evaluation that assesses the usefulness of the input modalities and software features, and validates the effectiveness of the MemTable system as a tool for assisting memory recall.

Abstract: In general, the subject matter described in this specification can be embodied in methods systems, and program products. A request to store data is received. The data is stored as an object in a repository. A request to create a table is received, where the request identifies a name for the table. The table is created with the name. A request to import the data into the table is received. The data is imported into the table, where importing the data in the object into the table includes converting the data in the object into columnar stripes, and storing the columnar stripes in association with the table. A request to perform a query on the table is received, where the request includes the query and identifies the table. The query is performed on the table, where performing the query includes querying one or more of the columnar stripes.

Abstract: A method begins by a processing module receiving a file retrieval request for a file, wherein the file includes one or more data regions, and wherein a data region of the one or more data regions is divided into a plurality of data segments and stored as a plurality of sets of encoded data slices in a dispersed storage network (DSN) memory. The method continues with the processing module retrieving a segment allocation table (SAT), wherein a SAT entry of a plurality of SAT entries includes information regarding storing the data region in the DSN memory and a segmentation scheme regarding the dividing of the data region into the plurality of data segments. The method continues with the processing module identifying the plurality of sets of encoded data slices and retrieving at least a sufficient number of the plurality of sets of encoded data slices to regenerate the data region.

Abstract: Table detection can be a valuable step in the analysis of unstructured documents. Although much work has been conducted in the domain of machine-print including books, scientific papers, etc., little has been done to address the case of handwritten inputs. In this paper, we study table detection in scanned handwritten documents subject to challenging artifacts and noise. First, we separate text components (machine-print, handwriting) from the rest of the page using an SVM classifier. We then employ a correlation-based approach to measure the coherence between adjacent text lines which may be part of the same table, solving the resulting page decomposition problem using dynamic programming. A report of preliminary results from ongoing experiments concludes the paper.

Abstract: Active data warehouses have emerged as a new business intelligence paradigm where data in the integrated repository is refreshed in near real-time. This shift of practices achieves higher consistency between the stored information and the latest updates, which in turn influences crucially the output of decision making processes. In this paper we focus on the changes required in the implementation of Extract Transform Load (ETL) operations which now need to be executed in an online fashion. In particular, the ETL transformations frequently include the join between an incoming stream of updates and a disk-resident table of historical data or metadata. In this context we propose a novel Semi-Streaming Index Join (SSIJ) algorithm that maximizes the throughput of the join by buffering stream tuples and then judiciously selecting how to best amortize expensive disk seeks for blocks of the stored relation among a large number of stream tuples. The relation blocks required for joining with the stream are loaded from disk based on an optimal plan. In order to maximize the utilization of the available memory space for performing the join, our technique incorporates a simple but effective cache replacement policy for managing the retrieved blocks of the relation. Moreover, SSIJ is able to adapt to changing characteristics of the stream (i.e. arrival rate, data distribution) by dynamically adjusting the allocated memory between the cached relation blocks and the stream. Our experiments with a variety of synthetic and real data sets demonstrate that SSIJ consistently outperforms the state-of-the-art algorithm in terms of the maximum sustainable throughput of the join while being also able to accommodate deadlines on stream tuple processing.

Abstract: The invention relates to a java server page (JSP) template-based intelligent table system capable of being defined freely, which controls a table through system configuration under the driving of a table engine to complete various service functions on the basis of data modeling. The (JSP) template-based intelligent table system provides implementing mechanisms of data modeling, service configuration, table drawing, table editing and the like and can realize user-oriented free customization of the table, and a user can realize most service logics by configuring various parameters through pages, thereby greatly improving the flexibility and extensibility of the intelligent table system. In the system, the JSP file is used as a template of the table, and various configuration information controlling the table and the table template are connected through the table engine, thus that the effect that the table service comes into effect as long as configured can be realized. In the part of table editing, the system can realize automatic calculation of table attribute, addition and delete of sub-table rows, secondary authority control of the table, service implementation and control of the table and the like.

Abstract: There is disclosed an interface enabling easy implementation of processing on hierarchical data, and a method facilitating comprehension of the structure, meaning, and format of hierarchical data. There is also disclosed a data processing device including a user interface portion receiving data with tuples each a combination of data of different types, and lists each a series of data of a same type, display data obtained by converting the data, and an instruction requesting processing from a user, and presenting the display data, data managing portion managing the hierarchical data, display-data generating portion generating the display data from the hierarchical data, and analyzing portion implementing the processing. Data is presented in a table format having rows displaying level names and attribute names, respectively. The generating portion samples data of each level of hierarchical data to extract an amount possible to present, and converts the data format.

Abstract: Systems and methods for replication replay in a relational database are disclosed. In one embodiment, a relational database includes a master database and a slave database, and events performed on the master database are stored in a log. A portion of the log is provided to a slave replay system associated with the slave database, and the slave replay system identifies a first table row associated with a first event of the portion of the log and a second table row associated with a second event in the portion of the log. The slave replay system replays the first and second events on the slave database in parallel if the first and second rows are different rows with unique sets of values, and otherwise replays the first and second events serially.

Abstract: Previous work has shown that using the GPU as a brute force method for SELECT statements on a SQLite database table yields significant speedups. However, this requires that the entire table be selected and transformed from the B-Tree to row-column format. This paper investigates possible speedups by traversing B+ Trees in parallel on the GPU, avoiding the overhead of selecting the entire table to transform it into row-column format and leveraging the logarithmic nature of tree searches. We experiment with different input sizes, different orders of the B+ Tree, and batch multiple queries together to find optimal speedups for SELECT statements with single search parameters as well as range searches. We additionally make a comparison to a simple GPU brute force algorithm on a row-column version of the B+ Tree.

Abstract: IBM Netezza is a powerful and highly parallelized Data Warehousing system that is simple to administer and to maintain. This system is an appliance that is purpose-built for data warehousing. The system is commonly referred to as data warehouse appliance that is designed specifically for running complex data warehousing workloads. The concept of an appliance is realized by integrating the database, server and the storage into an easy to deploy and manage system. In any database system the main bottle neck is IO. IBM Netezza reduces this bottleneck by using a commodity FPGA (Field-Programmable Gate Array) by pushing the SQL closer to silicon to help improve IO performance. This core component of the appliance is referred to as the Database Accelerator. The Database Accelerator along with the other components of the IBM Netezza appliance was discussed during a short high-level overview of the architecture. This overview was presented at the beginning of the workshop during a brief presentation. The presentation also included the basic usage on how to administer and maintain a Netezza database. The concepts covered in the presentation were reinforced by getting hands on experience using a Netezza appliance. Instead of using an actual IBM Netezza appliance a virtualized environment was provided with a lab manual outlining the steps and commands to run. The lab manual also included explanations for each of the step-by-step instructions used in the exercises. The agenda for the topics covered in the Hands-on-Lab exercises was: 1. Create Netezza Database Users and Groups (and set privileges) 2. Create the Workshop database 3. Create tables in the Workshop database 4. Load data into the Netezza Appliance with the nzload utility using the External Table framework 5. Examine the concept of Data Distribution to choose an appropriate Hash Key 6. Maintain a Netezza database using the groom command The workshop showed how simple it was to setup a IBM Netezza appliance after it has been delivered and configured. A factory-configured and installed IBM Netezza appliance includes some of the following components: • An IBM Netezza data warehouse appliance with pre-installed IBM Netezza software • A preconfigured Linux operating system (with Netezza modifications) • Several preconfigured Linux users and groups: c The nz user is the default Netezza system Administration account c The group is the default group • An IBM Netezza database user named ADMIN. The ADMIN user is the database super-user, and has full access to all system functions and objects • A preconfigured database group named PUBLIC. All database users are automatically placed in the group PUBLIC and therefore inherit all of its privileges The IBM Netezza appliance also includes a SQL dialect called Netezza Structured Query Language (NZSQL). You can use SQL commands to create and manage your Netezza databases, user access, and permissions for the databases, as well as to query and modify the contents of the databases. On a new IBM Netezza appliance, there is one main database, SYSTEM, and a database template, MASTER_DB. IBM Netezza uses the MASTER_DB as a template for all other user databases that are created on the system. Before creating the databases and tables, a brief explanation was provided about the virtualized environment used in the workshop. This also included how to connect to the Netezza appliance, which is completed through the Netezza SMP Host. Once connected to the Netezza appliance a set of new users were created, which were used for the remainder of the workshop. The concept of users and privileges were explored later when the database and tables were created. This would involve setting up a basic Security Access Model, which restricted or permitted certain actions to objects within the Netezza Appliance. After the Netezza Database Users were created the database and the tables for the workshop were created. Once the database and the tables are created, the next step as with any data warehouse environment is to load data into the tables in the database. This was easy by using the Netezza utility nzload which uses the External Table framework to efficiently load data in to a Netezza database. This framework contains more than one component, some of these components are: • External Tables -- These are tables stored as flat files on the host or client systems and registered like tables in the Netezza catalog. They can be used to load data into the Netezza appliance or unload data to the file system. • nzload -- This is a wrapper command line tool around external tables that provides an easy method loading data into the Netezza appliance. • Format Options -- These are options for formatting the data load to and from external tables. With a good understanding on how to create and populate tables in a Netezza database discussion followed on the importance of Data Distribution. Since IBM Netezza is built on a massively parallel architecture that distributes data and workloads over a large number of processing and data nodes, the single most important tuning factor is choosing the right distribution key. The distribution key governs which data rows of a table are distributed to a data slice and it is very important to choose an optimal distribution key to avoid data skew, processing skew and to make joins co-located whenever possible. This concept was so important that a separate section was devoted to this topic. The exercises examined how to pick the best Hash Key for distribution for each of the tables created in this workshop. During these set of exercises CTAS tables were utilized that showed how easy it is to change the Hash Key for a table without having to manually recreate and reload the data in the table. Before finishing the exercises for the day, one more important utility is explored to show how simple it is to maintain tables in a Netezza database. This utility was the GROOM command, which is used to reclaim free-space in a table. The concept of free-space in a Netezza database was illustrated by discussing how transactions are handled in a Netezza database. This included how SQL INSERTs, DELETEs, and UPDATEs are handled in a Netezza database. After learning these concepts, which were reinforced by the hands-on exercises provided in the workshop, you can now get an IBM Netezza Warehouse Appliance up-and-running after the appliance has been delivered and configured before being handed over to you.

Abstract: A system (100) for generating a computer readable data file representative of a mapping between a first representation of a set of concepts or of a data structure (e.g. a database schema) and a second representation of a set of concepts or of a data structure (e.g. an ontology), each representation comprising a plurality of complex representational elements (e.g. tables in a database schema and concepts in an ontology) each of which may itself include a number of associated subordinate representational elements (e.g. columns/fields of a table in a database schema and attributes of a concept in an ontology). The system (100) includes a semantic similarity calculation module (134) for calculating a semantic similarity measure between a subordinate element of the first representation and each of the subordinate elements in the second representation and a mapping generation module (137) for generating a mapping between the subordinate element of the first representation and one of the subordinate elements of the second representation selected in dependence upon the calculated semantic similarity measures between the subordinate elements.

Abstract: A system and method used to create a graphical user interface to a relational database, wherein the method for creating the graphical user interface (GUI) to a relational database, comprises: specifying a relationship between at least two tables (210): dynamically generating the GUI by creating layers of page frames according to the specified relationships (220): and displaying the GUI (230).

Abstract: A system for offer based restaurant reservations comprises a processor and a memory. The processor is configured to: receive a request for a reservation including a set of attributes such as a date or date range, a time range, a location, a cuisine, and a party size; determine a set of available reservations based on an actual table availability for the date, time, and the party size; determine one or more offers based on the date, time, and the party size; determine an overlapping subset between the set of available reservations and the one or more offers; and provide an indication of the overlapping subset. The memory is coupled to the processor and is configured to provide the processor with instructions.

Abstract: A business intelligence (BI) server maintains a plurality of metadata objects to support the extract, transform and load (ETL) processes. These metadata objects includes a transparent view object, which takes a joined set of source tables and represents a data shape of the joined set of source tables using a transformation, and a ETL mapping association object that maps the transformation contained in the transparent view object to a target table. The BI server can then orchestrate the movement of data from source systems into the target data warehouses in a source and target system agnostic way.