Snapshot Semantics for Temporal Multiset Relations (Extended Version)

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations and significantly outperforms such implementations on queries that involve aggregation.Comment: extended version of PVLDB pape

Temporal Stream Algebra

Data stream management systems (DSMS) so far focus on event queries and hardly consider combined queries to both data from event streams and from a database. However, applications like emergency management require combined data stream and database queries. Further requirements are the simultaneous use of multiple timestamps after different time lines and semantics, expressive temporal relations between multiple time-stamps and exible negation, grouping and aggregation which can be controlled, i. e. started and stopped, by events and are not limited to fixed-size time windows. Current DSMS hardly address these requirements. This article proposes Temporal Stream Algebra (TSA) so as to meet the afore mentioned requirements. Temporal streams are a common abstraction of data streams and data- base relations; the operators of TSA are generalizations of the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally. In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent blocking expressions on a "syntactical" level

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Closed-World Semantics for Query Answering in Temporal Description Logics

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. Many real-world applications, like processing electronic health records (EHRs), also contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. Our contribution consists of three main parts: Firstly, we introduce a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic ELH⊥, which is based on the minimal universal model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity. Secondly, we introduce a new temporal variant of ELH⊥ that features a convexity operator. We extend this minimal-world semantics for answering metric temporal conjunctive queries with negation over the logic and obtain similar rewritability and complexity results. Thirdly, apart from the theoretical results, we evaluate minimal-world semantics in practice by selecting patients, based their EHRs, that match given criteria

Enabling instant- and interval-based semantics in multidimensional data models: the T+MultiDim Model

Time is a vital facet of every human activity. Data warehouses, which are huge repositories of historical information, must provide analysts with rich mechanisms for managing the temporal aspects of information. In this paper, we (i) propose T+MultiDim, a multidimensional conceptual data model enabling both instant- and interval-based semantics over temporal dimensions, and (ii) provide suitable OLAP (On-Line Analytical Processing) operators for querying temporal information. T+MultiDim allows one to design typical concepts of a data warehouse including temporal dimensions, and provides one with the new possibility of conceptually connecting different temporal dimensions for exploiting temporally aggregated data. The proposed approach allows one to specify and to evaluate powerful OLAP queries over information from data warehouses. In particular, we define a set of OLAP operators to deal with interval-based temporal data. Such operators allow the user to derive new measure values associated to different intervals/instants, according to different temporal semantics. Moreover, we propose and discuss through examples from the healthcare domain the SQL specification of all the temporal OLAP operators we define. (C) 2019 Elsevier Inc. All rights reserved

Temporal Query Answering in DL-Lite over Inconsistent Data

In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic and extend to this setting three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for DL-LiteR temporal knowledge bases, and furthermore complete the picture for the consistent case

Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes: Extended Version

Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity

Semantic enabled complex event language for business process monitoring

Efforts are being made to enable business process monitoring and analysis through processing continuously generated events. Several ontologies and tools have been defined and implemented to allow applying general-purpose Business Process Analysis techniques to specific domains. On this basis, a Semantic Enabled Monitoring Event Language (SEMEL) is proposed to facilitate defining complex queries over monitoring data so as to interleave temporal and ontological reasoning. In this paper, the formal semantics of SEMEL is discussed, and the implementation approach of SEMEL interpreter is also briefly described, which encompasses translation into an operational language

Distributed Evaluation of Top-k Temporal Joins

To appear in SIGMOD'16We study a particular kind of join, coined Ranked Temporal Join (RTJ), featuring predicates that compare time intervals and a scoring function associated with each predicate to quantify how well it is satisfied. RTJ queries are prevalent in a variety of applications such as network traffic monitoring , task scheduling, and tweet analysis. RTJ queries are often best interpreted as top-k queries where only the best matches are returned. We show how to exploit the nature of temporal predicates and the properties of their associated scoring semantics to design TKIJ , an efficient query evaluation approach on a distributed Map-Reduce architecture. TKIJ relies on an offline statistics computation that, given a time partitioning into granules, computes the distribution of intervals' endpoints in each granule, and an online computation that generates query-dependent score bounds. Those statistics are used for workload assignment to reducers. This aims at reducing data replication, to limit I/O cost. Additionally , high-scoring results are distributed evenly to enable each reducer to prune unnecessary results. Our extensive experiments on synthetic and real datasets show that TKIJ outperforms state-of-the-art competitors and provides very good performance for n-ary RTJ queries on temporal data