BDEF : the behavioral design data exchange format

BDDB is a Behavioral Design Data Base that manages the design data produced and consumed by different behavioral synthesis tools. These different design tools retrieve design data from BDDB, manipulate the data, and then store the results back into the data base. BDDB thus needs to address the following two issues: (1) a design data exchange approach and (2) customized design data interfaces. To address the first issue, we have developed a textual description format for describing design data objects and relationships. This language, referred to as the Behavioral Design Data Exchange Format (BDEF), is used as common format for exchanging design data between BDDB and the design tools in the behavioral synthesis environment. To address the second issue, we have developed a behavioral object type description language (generally referred to as schema definition language) for describing the global data structures required by design tools as well as the desired design subviews of this global BDDB design information. One design view class, namely, BDEF, is the topic of this report.In this report we give a formal definition of the BDEF format. Then we describe a comprehensive example of applying BDEF to the behavioral synthesis domain. That is, we present the complete BDEF syntax for the Extended Control/Data Flow Graph Model (ECDFG), which is the design representation model used by most behavioral synthesis tools in the UCI CADLAB synthesis system. We also present several example descriptions of designs using this ECDFG model. A parser/graph compiler from BDEF into the generalized ECDFG design representation as well as a BDEF generator from the ECDFG data structures into the BDEF format have been implemented

The NASA Astrophysics Data System: Data Holdings

Since its inception in 1993, the ADS Abstract Service has become an indispensable research tool for astronomers and astrophysicists worldwide. In those seven years, much effort has been directed toward improving both the quantity and the quality of references in the database. From the original database of approximately 160,000 astronomy abstracts, our dataset has grown almost tenfold to approximately 1.5 million references covering astronomy, astrophysics, planetary sciences, physics, optics, and engineering. We collect and standardize data from approximately 200 journals and present the resulting information in a uniform, coherent manner. With the cooperation of journal publishers worldwide, we have been able to place scans of full journal articles on-line back to the first volumes of many astronomical journals, and we are able to link to current version of articles, abstracts, and datasets for essentially all of the current astronomy literature. The trend toward electronic publishing in the field, the use of electronic submission of abstracts for journal articles and conference proceedings, and the increasingly prominent use of the World Wide Web to disseminate information have enabled the ADS to build a database unparalleled in other disciplines. The ADS can be accessed at http://adswww.harvard.eduComment: 24 pages, 1 figure, 6 tables, 3 appendice

Database independent Migration of Objects into an Object-Relational Database

This paper reports on the CERN-based WISDOM project which is studying the serialisation and deserialisation of data to/from an object database (objectivity) and ORACLE 9i.Comment: 26 pages, 18 figures; CMS CERN Conference Report cr02_01

Archiving scientific data

We present an archiving technique for hierarchical data with key structure. Our approach is based on the notion of timestamps whereby an element appearing in multiple versions of the database is stored only once along with a compact description of versions in which it appears. The basic idea of timestamping was discovered by Driscoll et. al. in the context of persistent data structures where one wishes to track the sequences of changes made to a data structure. We extend this idea to develop an archiving tool for XML data that is capable of providing meaningful change descriptions and can also efficiently support a variety of basic functions concerning the evolution of data such as retrieval of any specific version from the archive and querying the temporal history of any element. This is in contrast to diff-based approaches where such operations may require undoing a large number of changes or significant reasoning with the deltas. Surprisingly, our archiving technique does not incur any significant space overhead when contrasted with other approaches. Our experimental results support this and also show that the compacted archive file interacts well with other compression techniques. Finally, another useful property of our approach is that the resulting archive is also in XML and hence can directly leverage existing XML tools

The CMIP6 Data Request (DREQ, version 01.00.31)

The data request of the Coupled Model Intercomparison Project Phase 6 (CMIP6) defines all the quantities from CMIP6 simulations that should be archived. This includes both quantities of general interest needed from most of the CMIP6-endorsed model intercomparison projects (MIPs) and quantities that are more specialized and only of interest to a single endorsed MIP. The complexity of the data request has increased from the early days of model intercomparisons, as has the data volume. In contrast with CMIP5, CMIP6 requires distinct sets of highly tailored variables to be saved from each of the more than 200 experiments. This places new demands on the data request information base and leads to a new requirement for development of software that facilitates automated interrogation of the request and retrieval of its technical specifications. The building blocks and structure of the CMIP6 Data Request (DREQ), which have been constructed to meet these challenges, are described in this paper