Data element

About: Data element is a research topic. Over the lifetime, 4731 publications have been published within this topic receiving 85274 citations. The topic is also known as: unit of data.

Custom common object

Abstract: Enterprise management system includes a product data structure (160), a related product line element (162), a list of price type element (164), a list of related inventory location element (166), a list of related product (168), a list of related business unit element (170), and a product custom data element (172). The system converts some or all the enterprise information that is in the source format into the target format by using an intermediate format of the enterprise information. The intermediate format includes a plurality of custom data type elements that are adapted for capturing unique customer information that are relevant to the customer's business systems. The intermediate form is then used to convert the enterprise information into the target format.

Metadata management system for an information dispersed storage system

Abstract: Briefly, the present invention relates to an information dispersal sytem in which original data to be stored is separated into a number of data 'slices' in such a manner that the data in each subset is less usable or less recognizable or completely unusable or completely unrecognizable by itself except when combined with some or all of the other data subsets. These data subsets are stored on separate storage devices as a way of increasing privacy and security. In accordance with an important aspect of the invention, a metadata management system stores and indexes user files across all of the storage nodes. A number of applications run on the servers supporting these storage nodes and are responsible for controlling the metadata. Metadata is the information about the data, the data slices or data subsets and the way in which these data subsets are dispersed among different storage nodes running over the network. As used herein, metadata includes data source names, their size, last modification date, authentication information etc. This information is required to keep track of dispersed data subsets among all the nodes in the system. Every time new data subsets are stored and old ones are removed from the storage nodes, the metadata is updated. In accordance with an important aspect of the invention, the metadata management system stores metadata for dispersed data where: the dispersed data is in several pieces; the metadata is in a separate dataspace from the dispersed data. Accordingly, the metadata management system is able to manage the metadata in a manner that is computationally efficient relative to known systems in order to enable broad use of the invention using the types of computers generally used by businesses, consumers and other organizations currently.

Methods And Systems For Managing Data

Abstract: Systems and methods for managing data, such as metadata. In one exemplary method, metadata from files created by several different software applications are captured, and the captured metadata is searched. The type of information in metadata for one type of file differs from the type of information in metadata for another type of file. Other methods are described and data processing systems and machine readable media are also described.

Querying Semi-Structured Data

Abstract: The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.