DynamiTE: Parallel Materialization of Dynamic RDF Data

Abstract

Abstract. One of the main advantages of using semantically annotated data is that machines can reason on it, deriving implicit knowledge from explicit information. In this context, materializing every possible implicit derivation from a given input can be computationally expensive, especially when considering large data volumes. Most of the solutions that address this problem rely on the assumption that the information is static, i.e., that it does not change, or changes very infrequently. However, the Web is extremely dynamic: online newspapers, blogs, social networks, etc., are frequently changed so that outdated information is removed and replaced with fresh data. This demands for a materialization that is not only scalable, but also reactive to changes. In this paper, we consider the problem of incremental materialization, that is, how to update the materialized derivations when new data is added or removed. To this purpose, we consider the ρdf RDFS fragment [11], and present a parallel system that implements a number of algorithms to quickly recalculate the derivation. In case new data is added, our system uses a parallel version of the well-known semi-naive evaluation of Datalog. In case of removals, we have implemented two algorithms, one based on previous theoretical work, and another one that is more efficient since it does not require a complete scan of the input for every update. We have evaluated the performance using a prototype system called DynamiTE , which organizes the knowledge bases with a number of indices to facilitate the query process and exploits parallelism to improve the performance. The results show that our methods are indeed capable to recalculate the derivation in a short time, opening the door to reasoning on much more dynamic data than is currently possible.

Heaven: A Framework for Systematic Comparative Research Approach for RSP Engines

“…To this extent, we first executed 14 experiments that involve the baselines as subjects and we showed that we cannot confirm the following two hypotheses, which have been already investigated top-down by the SR community [21,26,12,20]:…”

“…This decision is motivated because we want to provide reasoning capabilities and ρDF is the minimal meaningful task for a Stream Reasoner [26].…”

“…We need to stress the reasoning capabilities of the baseline in a regular way and LUBM is a recognized benchmark for the reasoning domain that has been used before in the SR field [26] 10 . Therefore, we configured D as follows: as dataset DS we opted for LUMB [14]; as data model M we opted for RDF Stream encoded as timestamped RDF Graphs; and as function F , since we want to keep the data stream regular, we chose one which keeps constant the number of triples in each Stimulus forming the RDF Stream and we set the application time unit of 100 ms.…”

Query Rewriting in RDF Stream Processing

“…using truth maintenance systems [23] and approximate reasoning optimized for memory consumption, by eliminating unnecessary intermediate results. Other works have also proposed parallelization techniques for the materialization of inferences in streaming knowledge-bases, although limited only to a fragment of RDFS [27]. On a similar path, works on knowledge evolution [17] have used DL reasoning over ontology streams to detect and explain the nature of the changes on the ontology, as well as potential inconsistencies.…”

Updating RDFS ABoxes and TBoxes in SPARQL

“…As for Sem mat 1 , we rely on a well-known technique in the area of updates for deductive databases called "delete and rederive" (DRed) [6,10,16,26,27]. Informally translated to our setting, when given a logic program and its materialisation T Ê , plus a set of rules A d to be deleted and a set of facts A i to be inserted, DRed (i) first deletes A d and all its effects (computed via semi-naive evaluation [25]…”