Identifying the Extent of Completeness of Query Answers over Partially Complete Databases

Abstract

In many applications including loosely coupled cloud databases, collaborative editing and network monitoring, data from multiple sources is regularly used for query answering. For reasons such as system failures, insufficient author knowledge or network issues, data may be temporarily unavailable or generally nonexistent. Hence, not all data needed for query answering may be available.In this paper, we propose a natural class of completeness patterns, expressed by selections on database tables, to specify complete parts of database tables. We then show how to adapt the operators of relational algebra so that they manipulate these completeness patterns to compute completeness patterns pertaining to query answers. Our proposed algebra is computationally sound and complete with respect to the information that the patterns provide. We show that stronger completeness patterns can be obtained by considering not only the schema but also the database instance and we extend the algebra to take into account this additional information. We develop novel techniques to efficiently implement the computation of completeness patterns on query answers and demonstrate their scalability on real data.

Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements1

“…To find statements that are potentially applicable to a frozen BGPP, we perform a hashmap lookup for each triple pattern of P and for all possible generalizations of that triple pattern where non-predicate terms are replaced by a variable. 23 Let us formalize the above sketch. Our main goal here is partial matching: retrieving only completeness statements having a triple pattern that can potentially be mapped to a triple in a frozen BGPP.…”

“…The problems of answer and pattern soundness are also completely new. In [23], Razniewski et al proposed completeness patterns and defined a pattern algebra to check the completeness of queries. The work incorporated database instances, yet provided only a sound algorithm for completeness checking.…”

Estimating the Impact of Unknown Unknowns on Aggregate Query Results

“…Recent works [21,41] defined database completeness in a partly open world semantic (i.e., database can be incomplete, which causes incorrect query results) and use the completeness information to denote the completeness of query results. Similar in spirit to our work, they investigate the impact on query results of entire database records that may be missing [41]; however, they also assume the knowledge of population size (e.g., there are 7 days in a week, there are this many cities in France) to define the completely missing records and measure the completeness.…”

“…Similar in spirit to our work, they investigate the impact on query results of entire database records that may be missing [41]; however, they also assume the knowledge of population size (e.g., there are 7 days in a week, there are this many cities in France) to define the completely missing records and measure the completeness.…”

Enabling Fine-Grained RDF Data Completeness Assessment

“…In the next section, we show that incorporating the content of RDF data may provide stronger inferences about query completeness. From the relational databases, Razniewski et al [4], proposed wildcard-based completeness patterns to provide completeness information over databases. To check query completeness, they defined a pattern algebra, which works upon database tables enriched with completeness patterns.…”