Ontology driven search of compound IDs

Abstract

Abstract. Object identification is a crucial step in most information systems. Nowadays, we have many different ways to identify entities such as surrogates, keys and object identifiers. However, not all of them guarantee the entity identity. Many works have been introduced in the literature for discovering meaningful identifiers (i.e., guaranteeing the entity identity according to the semantics of the universe of discourse), but all of them work at the logical or data level and they share some constraints inherent to the kind of approach. Addressing it at the logical level, we may miss some important data dependencies, while the cost to identify data dependencies purely at the data level may not be affordable. In this paper, we propose an approach for discovering meaningful identifiers driven by domain ontologies. In our approach, we guide the process at the conceptual level and we introduce a set of pruning rules for improving the performance by reducing the number of identifier hypotheses generated and to be verified with data. Finally, we also introduce a simulation over a case study to show the feasibility of our method.

Using Semantic Web Technologies for Exploratory OLAP: A Survey

“…Researchers prefer to use ad hoc algorithms on top of more mature Standard services. Thus, for example, Abelló and Romero [36], [37] incur higher Computation because of implementing ad hoc algorithms on top of the reasoner. We think that constraining or at least detecting some kind of expressions (e.g., MD queries) in the reasoners could be a solution to the puzzle (i.e., similar to detecting star-join patterns in relational query optimizers).…”

“…Abelló and Romero [36] examine the ontology to look for MD identifiers (in order to identify facts), which is known to be expensive in the general case due to the large amount of instances a DW must deal with. To yield a tractable complexity, this work proposes the use of a reference ontology to pre-identify candidates and drastically reduce the number of tests (i.e., data samplings) to be carried out.…”

Semantic metadata for supporting exploratory OLAP

“…This mechanism is already used in SW environments and BI 2.0 systems can strongly benefit from it. Furthermore, when having several metadata models related to the metamodel we can more efficiently sample metadata as the search space for the same elements is significantly reduced (see [3]) when starting from the metamodel types. This eliminates the need for classical sampling that is too computationally expansive for the large metadata volumes expected in BI 2.0.…”

“…The related properties are illustrated in Figure 5. 3 general, the roll-up from and hierarchy concepts can be inferred from a sequence of OLAP operations, however their explicit representations makes the ROLL-UP operation model self-contained such that it can be easily shared. The SM4MQ representation of the ROLL-UP example from Section 2 of aggregating data from the month to the year level over the running example schema is illustrated in Figure 5.4.…”

“…Although several open source platforms for business intelligence (BI) have emerged in the last decade, an open format to publish and share cubes among organizations is still missing. The Linked Data [45] initiative promotes sharing and reusing data on the web using semantic web (SW) standards and domain ontologies expressed in the Resource Description Framework (RDF) as the basic data representation layer for the SW [28], or in languages built on top of RDF (e.g., RDF-Schema [23] and OWL 3 ).…”