Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

Evaluation strategy for execution of SQL queries involving recursion and table queues

Abstract: The present invention provides an evaluation strategy for the execution of SQL queries involving recursion and table queues in a relational database management system. The table queue is established for the query in the memory of the computer, and tuples are generated by tuple-producing operators in the query for storage into the table queue. As operators of the query read the stored tuples from the table queue, a first end-of-file condition, termed a Temporary End-Of-File, occurs when all of the tuples currently stored in the table queue have been retrieved and control is returned to the operator reading from the table queue. A retry request for additional tuples is generated by the operator through the table queue to the tuple-producing operators. If the tuple-producing operators can generate additional tuples, then these additional tuples are stored into the table queue and control is returned to the operator reading from the table queue in response to the retry request. If the tuple-producing operators cannot generate the additional tuples, then a second end-of-file condition, termed a Permanent End-Of-File, occurs and control is returned to the operator reading from the table queue.

Tuple-Based Semantic and Structural Mapping for a Sustainable Interoperability

Abstract: Enterprises are demanded to collaborate and establish partnerships to reach global business and markets. However, due to the different sources of models and semantics, organizations are experiencing difficulties exchanging vital information electronically and seamlessly, even when they operate in related business environments. This situation is even worst in the advent of the evolution of the enterprise systems and applications, whose dynamics result in increasing the interoperability problem due to the continuous need for model adjustments and semantics harmonization. To contribute for a long term stable interoperable enterprise operating environment, the authors propose the integration of traceability functionalities in information systems as a way to support such sustainability. Either data, semantic, and structural mappings between partner enterprises in the complex network should be modelled as tuples and stored in a knowledge base for communication support with reasoning capabilities, thus allowing to trace, monitor and support the stability maintenance of a system’s interoperable state.

Self-adaptive, on-line reclustering of complex object data

Abstract: A likely trend in the development of future CAD, CASE and office information systems will be the use of object-oriented database systems to manage their internal data stores. The entities that these applications will retrieve, such as electronic parts and their connections or customer service records, are typically large complex objects composed of many interconnected heterogeneous objects, not thousands of tuples. These applications may exhibit widely shifting usage patterns due to their interactive mode of operation. Such a class of applications would demand clustering methods that are appropriate for clustering large complex objects and that can adapt on-line to the shifting usage patterns. While most object-oriented clustering methods allow grouping of heterogeneous objects, they are usually static and can only be changed off-line. We present one possible architecture for performing complex object reclustering in an on-line manner that is adaptive to changing usage patterns. Our architecture involves the decomposition of a clustering method into concurrently operating components that each handle one of the fundamental tasks involved in reclustering, namely statistics collection, cluster analysis, and reorganization. We present the results of an experiment performed to evaluate its behavior. These results show that the average miss rate for object accesses can be effectively reduced using a combination of rules that we have developed for deciding when cluster analyses and reorganizations should be performed.

Byte stream organization with improved random and keyed access to information structures

Abstract: The invention improves processing time when accessing information in a byte stream and avoids the step of deserializing unneeded portions of the byte stream when the byte stream encodes an information structure corresponding to a schema with arbitrarily nested lists and tuples. It facilitates efficient keyed access when lists of tuples represent tables with key columns by storing tables in nested column order, which extends the well-known concept of column-order so as to apply to arbitrarily nested tables. Using well-known offset calculation techniques within the nested lists that result from nested column order, the invention achieves greater efficiency by grouping together all scalar information items that correspond to the same node in a tree representation of the schema.

PerK: personalized keyword search in relational databases through preferences

Abstract: Keyword-based search in relational databases allows users to discover relevant information without knowing the database schema or using complicated queries. However, such searches may return an overwhelming number of results, often loosely related to the user intent. In this paper, we propose personalizing keyword database search by utilizing user preferences. Query results are ranked based on both their relevance to the query and their preference degree for the user. To further increase the quality of results, we consider two new metrics that evaluate the goodness of the result as a set, namely coverage of many user interests and content diversity. We present an algorithm for processing preference queries that uses the preferential order between keywords to direct the joining of relevant tuples from multiple relations. We then show how to reduce the complexity of this algorithm by sharing computational steps. Finally, we report evaluation results of the efficiency and effectiveness of our approach.