Adaptive web-based educational application for autistic students

Adaptive web-based applications have proven successful in reducing navigation and comprehension problems in hypermedia documents. In this paper, we describe a toolkit that is offered as an adaptive Web-based application to help autistic students incorporate to high education. The toolkit has been developed using a popular CMS in which we have integrated a client-side adaptation library. The toolkit described here was tried out during workshops with autistic students at Leeds Becketts University to gather (mostly qualitative) feedback on the adaptation and privacy aspects of the Autism&Uni platform. That feedback was later used to improve the toolkit

WiBAF into a CMS: Personalization in learning environments made easy

Adaptivity has proven successful in reducing navigation and comprehension problems in hypermedia documents. Authoring of adaptive hypermedia documents and especially of the adaptivity in these documents has been problematic or at least labour intensive throughout AH history. This paper shows how the integration of a CMS with an adaptive framework greatly simplifies the inclusion of personalization in existing educational applications. It does this within the context of European project Autism&Uni that uses adaptive hypermedia to offer information for students transitioning from high school to university, especially to cater for students on the autism spectrum as well as for non-autistic students. The use of our Within Browser adaptation framework (WiBAF) reduces privacy concerns because the user model is stored on the end-user's machine, and eliminates performance issues that currently prevent the adoption of adaptivity in MOOC platforms by having the adaptation performed on the end-user's machine as well (within the browser). Authoring of adaptive applications within the educational domain with the system proposed was tried out with first year students from the Design-Based Learning Hypermedia course at the Eindhoven University of Technology (TU/e) to gather feedback on the problems they faced with the platform

Context-Free Path Queries on RDF Graphs

Navigational graph queries are an important class of queries that canextract implicit binary relations over the nodes of input graphs. Most of the navigational query languages used in the RDF community, e.g. property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL, are based on the regular expressions. It is known that regular expressions have limited expressivity; for instance, some natural queries, like same generation-queries, are not expressible with regular expressions. To overcome this limitation, in this paper, we present cfSPARQL, an extension of SPARQL query language equipped with context-free grammars. The cfSPARQL language is strictly more expressive than property paths and nested expressions. The additional expressivity can be used for modelling graph similarities, graph summarization and ontology alignment. Despite the increasing expressivity, we show that cfSPARQL still enjoys a low computational complexity and can be evaluated efficiently.Comment: 25 page

Scalable indexing of RDF graphs for efficient join processing

Current approaches to RDF graph indexing suffer from weak data locality, i.e., information regarding a piece of data appears in multiple locations, spanning multiple data structures. Weak data locality negatively impacts storage and query processing costs. Towards stronger data locality, we propose a Three-way Triple Tree (TripleT) secondary memory indexing technique to facilitate flexible and efficient join evaluation on RDF data. The novelty of TripleT is that the index is built over the atoms occurring in the data set, rather than at a coarser granularity, such as whole triples occurring in the data set; and, the atoms are indexed regardless of the roles (i.e., subjects, predicates, or objects) they play in the triples of the data set. We show through extensive empirical evaluation that TripleT exhibits multiple orders of magnitude improvement over the state-of-the-art, in terms of both storage and query processing costs

GraDES-NDA 2019:joint international workshop on graph data management experiences & systems and network data analytics

\u3cp\u3eGRADES-NDA 2019 is the second joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The focus of GRADES-NDA is the application areas, usage scenarios, and open challenges in managing large-scale graph-shaped data. To summarize, GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on massive graphs.\u3c/p\u3

On directly mapping relational databases to property graphs

\u3cp\u3eMuch of the data found in practice resides in relational DBs. However, many contemporary analytical tasks are performed on graphs. Property graphs are currently one of the most prevalent data models for graph data management in industry. Therefore, a key challenge is to understand the fundamental relationships between relational databases and property graph databases. This paper reports our ongoing work towards understanding these relationships by proposing R2PG-DM, a direct mapping of relational databases to property graphs. Given a relational database schema and instance, a direct mapping generates a corresponding property graph instance. The semantics of our mapping is defined using Datalog. Our work is inspired by existing approaches for direct mappings of relational databases into earlier graph data models. Future work is to study our mapping with respect to fundamental properties such as information and query preservation.\u3c/p\u3

4th International Workshop on Logic in Databases, LID 2011; Uppsala; Sweden; 25 March 2011 through 25 March 2011:Foreword

Not available

An experimental study of context-free path query evaluation methods

\u3cp\u3eContext-free path queries extend regular path queries for increased expressiveness. A context-free grammar is used to recognize accepted paths by their label strings, or traces. Such queries arise naturally in graph analytics, e.g., in bioinformatics applications. Currently, the practical performance of methods for context-free path query evaluation is not well understood. In this work, we study three state of the art context-free path query evaluation methods. We measure the performance of these methods on diverse query workloads on various data sets and compare their results. We showcase how these evaluation methods scale as graphs get bigger and queries become larger or more ambiguous. We conclude that state of the art solutions are not able to cope with large graphs as found in practice.\u3c/p\u3

On structure preserving sampling and approximate partitioning of graphs

Massive graphs are becoming increasingly common in a variety of domains such as social networks and web analytics. One approach to overcoming the challenges of size is to sample the graph, and perform analytics on the smaller graph. However, to be useful, the sample must maintain the properties of interest in the original graph. In this paper, we analyze the quality of five representative sampling algorithms in how well they preserve graph structure, the bisimulation structure of graphs in particular. As part of this study, we also develop a new scalable algorithm for computing bisimulation partitions of massive graphs. We empirically demonstrate the superior performance of our new algorithm in both sequential and distributed settings

A survey of benchmarks for graph-processing systems

Benchmarking is a process that informs the public about the capabilities of systems-under-test, focuses on expected and unexpected system-bottlenecks, and promises to facilitate system tuning and new systems designs. In this chapter, we survey benchmarking approaches for graph-processing systems. First, we describe the main features of a benchmark for graph-processing systems. Then, we systematically survey across these features a diverse set of benchmarks for RDF databases, benchmarks for graph databases, benchmarks for parallel and distributed graph-processing systems, and data-only benchmarks. We trace in our survey not only the important benchmarks, but also their innovative approaches and how their core ideas evolved from previous benchmarking approaches. Last, we identify ongoing and future research directions for benchmarking initiatives