Application-Specific Schema Design for Storing Large RDF Datasets

We offer the following features: (1) The summary is a RDF graph it- self, which allows us to post simplified queries towards the summarizations using the same techniques (e.g.

As pattern mining algorithms deal with symbolic sequences, we present rst how to transform time series related to audio data into symbolic sequences.. Then we show how to use

In Section 4, we use a high-performing blocking method called block purging from the original AC paper as a baseline for evaluating the proposed Sorted Neighborhood workflow..

Considering that Facebook exports a JSON data dump, and that Twitter similarly will import 23 a JSON data dump - the process of transforma- tions will involve: (i) uplift

To speed up the execution of the clustering, SC- DBSCAN first partitions the data, identifies the core patterns, builds the clusters in parallel in each partition and, finally,

Our primary research question is the following: is it possible to devise and im- plement an efficient horizontal partitioning strategy for a CS-based system, both for centralized

We study the relational to RDF data exchange problem, where the tar- get constraints are specified using Shape Expression schema (ShEx). We investi- gate two fundamental problems:

It has been explained that Subject orientation, data integration, non-volatility of data, and time variations are the key issues under consideration that can give base to