Publicación: CONSISTENT QUERY ANSWERING IN DATA WAREHOUSES
| dc.creator | MÓNICA ALEJANDRA CANIUPÁN MARILEO | |
| dc.date | 2009 | |
| dc.date.accessioned | 2025-01-10T15:01:30Z | |
| dc.date.available | 2025-01-10T15:01:30Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | A DATA WAREHOUSE (DW) IS A DATA REPOSITORY THAT ORGANIZES AND PHYSICALLY INTEGRATES DATA FROM MULTIPLE SOURCES UNDER SPECIAL KINDS OF SCHEMAS. A DW IS COMPOSED BY A SET OF DIMENSIONS THAT REFLECT THE WAY THE DATA IS STRUCTURED, AND THE FACTS THAT CORRESPOND TO QUANTITATIVE DATA RELATED WITH THE DIMENSIONS. A DIMENSION SCHEMA IS A HIERARCHICAL GRAPH OF CATEGORIES. A DIMENSION INSTANCE IS STRICT IF EVERY ELEMENT OF THE DIMENSION HAS A UNIQUE ANCESTOR ELEMENT IN EACH OF THE ANCESTOR CATEGORIES. THIS PROPERTY IS CRUCIAL FOR THE EFFICIENCY OF THE SYSTEM SINCE IT ALLOWS FOR THE CORRECT COMPUTATION OF AGGREGATE QUERIES USING PRE-COMPUTED VIEWS. A DIMENSION INSTANCE MAY BECOME NON-STRICT AFTER UPDATE OPERATIONS. WHEN THIS HAPPENS, THE INSTANCE CAN BE MINIMALLY REPAIRED IN SEVERAL WAYS. IN THIS PAPER WE CHARACTERIZE CONSISTENT ANSWERS TO AGGREGATE QUERIES BY MEANS OF SMALLEST RANGES THAT CONTAIN THE ANSWERS OBTAINED FROM EVERY MINIMAL REPAIR. WE ALSO INTRODUCE THE NOTION OF CANONICAL DIMENSION WHICH CAPTURES INFORMATION ABOUT ALL THE MINIMAL REPAIRS. WE USE THIS DIMENSION TO APPROXIMATE CONSISTENT QUERY ANSWERS. | |
| dc.format | application/pdf | |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.uri | https://repositorio.ubiobio.cl/handle/123456789/10055 | |
| dc.language | spa | |
| dc.publisher | PROCEEDINGS CONGRESS OF LATIN AMERICAN WOMEN IN COMPUTING, PERÚ | |
| dc.rights | PUBLICADA | |
| dc.title | CONSISTENT QUERY ANSWERING IN DATA WAREHOUSES | |
| dc.type | ARTÍCULO | |
| dspace.entity.type | Publication | |
| ubb.Estado | PUBLICADA | |
| ubb.Otra Reparticion | DEPARTAMENTO DE SISTEMAS DE INFORMACION | |
| ubb.Sede | CONCEPCIÓN |
