Sana Sellami

A new Spatial OLAP approach for the analysis of Volunteered Geographic Information

Abstract Volunteered Geographic Information (VGI) has great potential for enhancing analysts’ capabilities, assuming that VGI data quality issues, such as credibility and precision, are properly addressed. In this paper, we study the integration of VGI in Spatial OLAP (SOLAP) systems, which allow the integration and analysis of large volumes of good quality data. Using a real-world scenario, we highlight some similarities and differences among these two types of systems. We define a conceptual quality-oriented framework for warehousing and OLAPing VGI data. In particular, to address precision and credibility problems related to VGI data, we propose two new ETL operators: aggregation based on the VGI credibility and a filter based on the historical precision. We also define a new spatio-multidimensional model that provides decision makers with a global description of the quality of the aggregated data. To validate our proposal, we extend the classical relational SOLAP architecture using a standard VGI system.

From Volunteered Geographic Information to Volunteered Geographic OLAP: A VGI Data Quality-Based Approach

Volunteered Geographic Information (VGI) represents a valuable source of information. At the same time this data presents new data quality issues such as credibility. In this paper, we investigate the integration of VGI in Spatial OLAP (SOLAP) systems, which allow analyzing huge volume of geographic datasets. By means of a real case study, we highlight the similarities and differences of these two kinds of systems. We propose a methodology to handle VGI data quality issues during the warehousing and OLAPing phases. In particular, we define a new aggregation ETL operator based on the VGI credibility. We also present a spatio-multidimensional model that provides decision-makers with a general description of the correctness of the SOLAP aggregations.

Towards a Flexible Schema Matching Approach for Semantic Web Service Discovery

Semantic web service discovery has attracted a lot of attention in the last decade. Research conducted in this area can be (mainly) summarized as follows: (1) monolith matchmaking algorithms (and systems), and (2) schema matching-based techniques. In this paper we describe a flexible approach that takes leverage of existing schema matchers, leading to a multiple choice strategy for semantic service discovery. The approach has been implemented and validated in using the data collection provided by the S3 (Semantic Service Selection) community, and led to promising preliminary results.

Flexible Matchmaking for RESTful Web Services

This paper describes a flexible matchmaking approach that relies on an extensible set of schema/ontology matching techniques and primitives that may be combined in various ways, accordingly with the specification of a service request. The approach has been implemented and tested against a collection of RESTFul web services described in hRESTS microformat.

Semantic annotation: what about quality?

This paper outlines an ongoing research focused on the quality analysis of semantic annotation towards better Web Services management. Considering the properties of the annotation process and result, we provide an overview of the main issues related to the semantic annotation of Web Services and our first clues to tackle this relevant issue.

Web Services Discovery and Composition: A Schema Matching Approach

Automated matching of service descriptions is the key to service discovery and composition. In this paper, we propose an approach for web services discovery and composition. The approach relies on (1) SAWSDL, a simple and generic annotation language, (2) an XML representation of a web service that carries both syntactic (e.g., WSDL) and semantic (e.g., SAWSDL) information, and (3) the reuse of available schema matchers. The approach departs from exiting ones because it does not advocate a specific matchmaking algorithm, and it promotes the combination of different schema matchers, allowing multiple discovery and composition strategies.

Leveraging Track Relationships for Web Service Recommendation

Existing Web services recommendation approaches are based on usage statistics or QoS properties, leaving aside the evolution of the services ecosystem. These approaches do not always capture new or more recent users preferences resulting in recommendations with possibly obsolete or less relevant services. In this paper, we describe a novel Web services recommendation approach where the services ecosystem is represented as a heterogeneous multi-graph, and edges may have different semantics. The recommendation process relies on data mining techniques to suggest services of interest to a user.

A Multigraph Approach for Web Services Recommendation

In this paper, we describe a Web services recommendation approach where the services’ ecosystem is represented as a heterogeneous multigraph, and edges may have different semantics. The recommendation process relies on clustering techniques to suggest services “of interest” to a user. Our approach has been implemented as a tool called WesReG (Web services Recommendation with Graphs) on top of Neo4j and its cypher query language. We present the system implementation details and present the results of experiments on a collection of real Web services.

WSTP: Web Services Tagging Platform

Recently tagging has been employed to improve the performance of service discovery. Two main challenges have to be addressed when tags are used in Web service discovery: tag relevancy and tag sense disambiguation. In this paper, we present our Web service tagging platform that addresses these problems and allows a semantic search of tagged Web services.

Modeling and Retrieving Linked RESTful APIs: A Graph Database Approach

This paper describes an approach that combines Linked Data and Graph Database concepts for modeling and retrieving RESTFul Linked APIs Web APIs. We propose a multi-levels graph structure where the Web APIs vertices are connected by means of different types of links: The graph is stored in a graph database to allow graph exploration, e.g., to achieve an API discovery task for instance. The exploration/retrieval process is performed by means of graph queries: on the user client side, a keyword search interface is provided while, on the server side graph database, a graph query is issued. The approach has been implemented on top of Neo4j and its cypher query language, and an experimentation has been conducted with real datasets.