Logic, databases and parallel computation (Information      and Computer Science)
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Jolita D. Middleton', 'PO_EMAI': None, 'PO_PHON': None}
The goal of this research is to develop and test efficient,                parallel  evaluation methods for database and knowledge base query              languages. We will use the theory of parallel algorithms to identify            and realize  the potential parallelism in new, logic-based query                languages.  For this we will investigate the fine-grained parallelism           of primitive  symbolic operations such as unification, term matching,           congruence closure and special fixpoint operators. We will develop              and implement parallel  algorithms for recursive logical queries on             state-of-the-art multiprocessor systems. We will also examine the use           of equational theorem-proving techniques for database manipulation              and design.  The efficient integration of such techniques with                  recursive logical queries is an important step in  combining                    functional and logic programming in a structured database context.                                                                                                   In recent years, basic query languages, such as the relational             calculus, have been augmented by adding new programming features.               With these new features, database and knowledge base languages are              now moving closer to full programming languages. The optimization and           efficient evaluation of these languages is crucial, if one is to                combine the high performance of database technology with the power of           computing methods from artificial intelligence. New parallel                    (multiprocessor) machines offer the raw computational power for                 realizing this goal.  But to harness that raw power for database and            knowledge base applications, completely new algorithmic techniques -            adapted to parallel evaluation - are essential.  Such techniques will           be developed by this research.
{'FirstName': 'Paris', 'LastName': 'Kanellakis', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Paris Kanellakis', 'EmailAddress': 'pck@cs.brown.edu', 'NSF_ID': '000105467', 'StartDate': '05/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Brown University', 'CityName': 'PROVIDENCE', 'ZipCode': '029129100', 'PhoneNumber': '4018632777', 'StreetAddress': '1 PROSPECT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Rhode Island', 'StateCode': 'RI', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'RI01', 'ORG_UEI_NUM': 'E3FDXZ6TBHW3', 'ORG_LGL_BUS_NAME': 'BROWN UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1986&All=true', 'xml': '8617344.xml'}