105 research outputs found
PG-Triggers: Triggers for Property Graphs
Graph databases are emerging as the leading data management technology for
storing large knowledge graphs; significant efforts are ongoing to produce new
standards (such as the Graph Query Language, GQL), as well as enrich them with
properties, types, schemas, and keys. In this article, we propose PG-Triggers,
a complete proposal for adding triggers to Property Graphs, along the direction
marked by the SQL3 Standard. We define the syntax and semantics of PG-Triggers
and then illustrate how they can be implemented on top of Neo4j, one of the
most popular graph databases. In particular, we introduce a syntax-directed
translation from PG-Triggers into Neo4j, which makes use of the so-called APOC
triggers; APOC is a community-contributed library for augmenting the Cypher
query language supported by Neo4j. We also illustrate the use of PG-Triggers
through a life science application inspired by the COVID-19 pandemic. The main
result of this article is proposing reactive aspects within graph databases as
first-class citizens, so as to turn them into an ideal infrastructure for
supporting reactive knowledge management.Comment: 12 pages, 4 figures, 3 table
Crowdsourcing for Top-K Query Processing over Uncertain Data
Querying uncertain data has become a prominent application due to the proliferation of user-generated content from social media and of data streams from sensors. When data ambiguity cannot be reduced algorithmically, crowdsourcing proves a viable approach, which consists of posting tasks to humans and harnessing their judgment for improving the confidence about data values or relationships. This paper tackles the problem of processing top- K queries over uncertain data with the help of crowdsourcing for quickly converging to the realordering of relevant results. Several offline and online approaches for addressing questions to a crowd are defined and contrasted on both synthetic and real data sets, with the aim of minimizing the crowd interactions necessary to find the realordering of the result set
Processing keyword queries under access limitations
The Deep Web is constituted by data accessible through Web pages, but not readily indexable by search engines, as they are returned in dynamic pages. In this paper we propose a framework for accessing Deep Web sources, represented as relational tables with so-called access limitations, with keyword-based queries. We formalize the notion of optimal answer and propose methods for query processing. To the best of our knowledge, ours is the first systematic approach to keyword search in such context
- …