Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda

The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a service-oriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research

A Service-Oriented Approach for Network-Centric Data Integration and Its Application to Maritime Surveillance

Maritime-surveillance operators still demand for an integrated maritime picture better supporting international coordination for their operations, as looked for in the European area. In this area, many data-integration efforts have been interpreted in the past as the problem of designing, building and maintaining huge centralized repositories. Current research activities are instead leveraging service-oriented principles to achieve more flexible and network-centric solutions to systems and data integration. In this direction, this article reports on the design of a SOA platform, the Service and Application Integration (SAI) system, targeting novel approaches for legacy data and systems integration in the maritime surveillance domain. We have developed a proof-of-concept of the main system capabilities to assess feasibility of our approach and to evaluate how the SAI middleware architecture can fit application requirements for dynamic data search, aggregation and delivery in the distributed maritime domain

Performance Evaluation of Hierarchical Publish-Subscribe Monitoring Architecture for Service-Oriented Applications

Abstract -Contemporary high-performance service-oriented applications demand a performance efficient run-time monitoring. In this paper, we analyze a hierarchical publishsubscribe architecture for monitoring service-oriented applications. The analyzed architecture is based on a tree topology and publish-subscribe communication model for aggregation of distributed monitoring data. In order to satisfy interoperability and platform independence of serviceorientation, monitoring reports are represented as XML documents. Since XML formatting introduces a significant processing and network load, we analyze the performance of monitoring architecture with respect to the number of monitored nodes, the load of system machines, and the overall latency of the monitoring system

Reasoning About a Service-oriented Programming Paradigm

This paper is about a new way for programming distributed applications: the service-oriented one. It is a concept paper based upon our experience in developing a theory and a language for programming services. Both the theoretical formalization and the language interpreter showed us the evidence that a new programming paradigm exists. In this paper we illustrate the basic features it is characterized by

OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput

Context-Aware Information Retrieval for Enhanced Situation Awareness

In the coalition forces, users are increasingly challenged with the issues of information overload and correlation of information from heterogeneous sources. Users might need different pieces of information, ranging from information about a single building, to the resolution strategy of a global conflict. Sometimes, the time, location and past history of information access can also shape the information needs of users. Information systems need to help users pull together data from disparate sources according to their expressed needs (as represented by system queries), as well as less specific criteria. Information consumers have varying roles, tasks/missions, goals and agendas, knowledge and background, and personal preferences. These factors can be used to shape both the execution of user queries and the form in which retrieved information is packaged. However, full automation of this daunting information aggregation and customization task is not possible with existing approaches. In this paper we present an infrastructure for context-aware information retrieval to enhance situation awareness. The infrastructure provides each user with a customized, mission-oriented system that gives access to the right information from heterogeneous sources in the context of a particular task, plan and/or mission. The approach lays on five intertwined fundamental concepts, namely Workflow, Context, Ontology, Profile and Information Aggregation. The exploitation of this knowledge, using appropriate domain ontologies, will make it feasible to provide contextual assistance in various ways to the work performed according to a user’s taskrelevant information requirements. This paper formalizes these concepts and their interrelationships

Non-functional Property based service selection: A survey and classification of approaches

In recent years there has been much effort dedicated to developing approaches for service selection based on non-functional properties. It is clear that much progress has been made, and by considering the individual approaches there is some overlap in functionality, but obviously also some divergence. In this paper we contribute a classification of approaches, that is, we define a number of criteria which allow to differentiate approaches. We use this classification to provide a comparison of existing approaches and in that sense provide a survey of the state of the art of the field. Finally we make some suggestions as to where the research in this area might be heading and which new challenges need to be addressed