Carmela Comito

Grid services: principles, implementations and use

The Open Grid Services Architecture (OGSA) defines the Grid service concept using principles from both the Grid computing and Web Services models. OGSA lets developers integrate resources and services across distributed, heterogeneous, and dynamic environments. This paper explores OGSA and Grid service concepts by discussing both current implementations and recently proposed standards, such as the Web Services Resource Framework (WSRF). To show the benefits of the OGSA approach, we present the Grid Data Integration System (GDIS), an OGSA based architecture for data integration on Grids. Data integration on Grids is a challenging issue due to the large scale, dynamic, autonomous and distributed nature of data resources. GDIS exploits OGSA services to support federation, analysis, and processing of data from different distributed sources.

XML data integration in OGSA grids

Data integration is the flexible and managed federation, analysis, and processing of data from different distributed sources. Data integration is becoming as important as data mining for exploiting the value of large and distributed data sets that are available today. Distributed processing infrastructures such as Grids can be used for data integration on geographically distributed sites. This paper presents a framework for integrating heterogeneous XML data sources distributed among the nodes of a Grid. We propose a query reformulation algorithm to combine and query XML documents through a decentralized point-to-point mediation process among the different data sources based on schema mappings. The above cited XML integration formalism is exposed as a Grid Service within the GDIS architecture. GDIS is a service-based architecture for providing data integration in Grids using a decentralized approach. The underlying model of such architecture is discussed and we show how it fits the XMAP formalism/algorithm.

Integrating ontology and workflow in PROTEUS, a grid-based problem solving environment for bioinformatics

Bioinformatics is as a bridge between life science and computer science: computer algorithms are needed to face complexity of biological processes. Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. We discuss requirements of such applications and present the architecture of PROTEUS, a grid-based problem solving environment that integrates ontology and workflow approaches to enhance composition and execution of bioinformatics applications on the grid.

A service-oriented system for distributed data querying and integration on Grids

Data Grids rely on the coordinated sharing of and interaction across multiple autonomous database management systems. They provide transparent access to heterogeneous and autonomous data resources stored on Grid nodes. Data sharing tools for Grids must include both distributed query processing and data integration functionality. This paper presents the implementation of a data sharing system that (i) is tailored to data Grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation to retrieve semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and presents empirical evaluation results.

A Service-Oriented System to Support Data Integration on Data Grids

Data Grids provide transparent access to heterogeneous and autonomous data resources. The main contribution of this paper is the presentation of a data sharing system that (i) is tailored to data grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation for retrieving semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and also presents empirical evaluation results.

Mining human mobility patterns from social geo-tagged data

Abstract Online social networks allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of posts/tweets manifest the spatial–temporal movements of people in real life. This paper aims to analyze such movements to discover people and community behavior. To this end, we defined and implemented a novel methodology to mine popular travel routes from geo-tagged posts. Our approach infers interesting locations and frequent travel sequences among these locations in a given geo-spatial region, as shown from the detailed analysis of the collected geo-tagged data.

Towards a Cloud-Based Framework for Urban Computing, The Trajectory Analysis Case

In this paper we present a Cloud-based framework for urban computing that can be tailored to be used in different scenarios of urban planning and management that can occur in smart cities. The focus in the paper is on the management of large-scale socio-geographic data obtained through the trajectories followed by mobile devices. Our goal is to mine human activities and routines from this socio-geographic data in order to catch users behaviour. To this aim, we introduce a methodology for trajectory pattern mining consisting in (a) finding frequent regions, more densely passed through ones, and (b) extracting trajectory patterns from those regions. Experimental evaluation shows that due to complexity and large data involved in the application scenario, the trajectory pattern mining process can take advantage from a parallel execution environment offered by a Cloud architecture.

A Semantic Overlay Network for P2P Schema-Based Data Integration

Today data sources are pervasive and their number is growing tremendously. Current tools are not prepared to exploit this unprecedented amount of information and to cope with this highly heterogeneous, autonomous and dynamic environment. In this paper, we propose a novel semantic overlay network architecture, PARIS, aimed at addressing these issues. In PARIS, the combination of decentralized semantic data integration with gossip-based (unstructured) overlay topology management and (structured) distributed hash tables provides the required level of flexibility, adaptability and scalability, and still allows to perform rich queries on a number of autonomous data sources. We describe the logical model that supports the architecture and show how its original topology is constructed. We present the usage of the system in detail, in particular, the algorithms used to let new peers join the network and to execute queries on top of it and show simulation results that assess the scalability and robustness of the architecture.

PROTEUS: a Bioinformatics Problem Solving Environment on Grids

Bioinformatics can be considered as a bridge between life science and computer science, where high performance computational platforms and software are required to manage complex biological data. In this paper we present PROTEUS, a Grid-based Problem Solving Environment that integrates ontology and workflow approaches to enhance composition and execution of bioinformatics application on the Grid. Architecture and preliminary experimental results are reported.

Trajectory Pattern Mining for Urban Computing in the Cloud

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, and sensors, allows for the collections of large amounts of movement data. This amount of data can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. From a technological viewpoint, Cloud computing can play an essential role by helping city administrators to quickly acquire new capabilities and reducing initial capital costs by means of a comprehensive pay-as-you-go solution. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, in a Cloud-based framework. Experimental evaluation has been carried out on both real-world and synthetic trajectory data, up to one million of trajectories. The results show that, due to the high complexity and large volumes of data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture in terms of both execution time, speed-up and scale-up.