Data access

About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.

Systems and methods of accessing and updating recorded data via an inter-object proxy

Abstract: Some embodiments provide a system including a database, two or more data access objects, and a proxy object. In some such embodiments, each data access object has exclusive access privileges to a subset of data within the database. Further each object includes one or more of a read method that provides access to data and a write method that causes data to be stored. In such embodiments, the proxy object accesses data via data access object methods and includes a valuate method, the execution of which causes data to be passed from a data access object to the proxy object.

Broadcast data allocation for efficient access of multiple data items in mobile environments

Abstract: The issue of data broadcast has received much attention in mobile computing. A periodic broadcast of frequently requested data can reduce the workload of the up-link channel and facilitate data access for the mobile user. Many approaches have been proposed to schedule data items for broadcasting. However, the issues of accessing multiple data items on the broadcast channel are less discussed. Two problems are discussed in this paper, that is, deciding the content of the broadcast channel based on the queries from the clients, and scheduling the data items to be broadcast. We will show that these two problems are NP-complete. Different heuristics to these problems are presented and compared through performance evaluations.

Biological data integration: wrapping data and tools

Abstract: Scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data access, analysis, and visualization tools. Building a digital library for scientific data requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web as well as data generated by software. We present an approach to wrapping web data sources, databases, flat files, or data generated by tools through a database view mechanism. Generally, a wrapper has two tasks: it first sends a query to the source to retrieve data and, second builds the expected output with respect to the virtual structure. Our wrappers are composed of a retrieval component based on an intermediate object view mechanism called search views mapping the source capabilities to attributes, and an Extensible Markup Language (XML) engine, respectively, to perform these two tasks. The originality of the approach consists of: 1) a generic view mechanism to access seamlessly data sources with limited capabilities and 2) the ability to wrap data sources as well as the useful specific tools they may provide. Our approach has been developed and demonstrated as part of the multidatabase system supporting queries via uniform object protocol model (OPM) interfaces.

Technique for providing access to data

Abstract: A data retrieval system provides data to a user of a client computer connected to multiple data stores and multiple other computers. A request for data is received at the client computer. The request is forwarded from the client computer to a server computer and intercepted at a reverse proxy caching connection. An attempt is made to locate the data at a data store at the reverse proxy caching connection. If the data is not found the request is forwarded to the server computer. In order to provide data to a user. A user interface is provided. Initially, data elements associated with a grouping of data elements are identified. Then, a subset of the selected data elements are selected based on weights associated with the data elements, without selecting more than a specified number of data elements that are associated with a same sub-category.

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

Abstract: As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as increasingly heterogeneous data formats. Still, state-of-the-art approaches typically rely on copying and transforming data into one (or few) repositories. Queries, on the other hand, are often ad-hoc and supported by pre-cooked operators which are not adaptive enough to optimize access to data. As data formats and queries increasingly vary, there is a need to depart from the current status quo of static query processing primitives and build dynamic, fully adaptive architectures. We build ViDa, a system which reads data in its raw format and processes queries using adaptive, just-in-time operators. Our key insight is use of virtualization, i.e., abstracting data and manipulating it regardless of its original format, and dynamic generation of operators. ViDa’s query engine is generated just-in-time; its caches and its query operators adapt to the current query and the workload, while also treating raw datasets as its native storage structures. Finally, ViDa features a language expressive enough to support heterogeneous data models, and to which existing languages can be translated. Users therefore have the power to choose the language best suited for an analysis.