Manuel Lama

Automatic Web Service Composition with a Heuristic-Based Search Algorithm

Service Oriented Architectures and web service technology are becoming popular in recent years. As more web services can be used over the Internet, the need to find efficient algorithms for web services composition that can deal with large amounts of services becomes important. These algorithms must deal with different issues like performance, semantics or user restrictions. In this paper we present an A* algorithm which solves the problem of semantic input-output message structure matching for web service composition. Given are quest, a service dependency graph with a subset of the original services from an external repository is dynamically generated. Then, the A*search algorithm is used to find a minimal composition that satisfies the user request. Moreover, in order to improve the performance, a set of dynamic optimization techniques has been implemented over the search process. A full experimental validation with eight different public repositories has been done showing a good performance as in all tests as the algorithm finds a valid solution with minimal number of services and execution path.

Intelligent telemonitoring of critical-care patients

Sutil+ is an intelligent monitoring system that is under development. The aim is to endow the system with more intelligent behaviour and to make the presentation of information to the user as flexible as possible, in space, time, and form. We present two telemonitoring solutions that enable access to information resulting from the monitoring of patients in coronary care units, independent of the location of the system user. We concentrate on aspects of data acquisition and storage and, above all, interaction with the user.

ODE SWS: a framework for designing and composing semantic Web services

Specifying semantic Web services requires designing the semantic description at a conceptual level to guarantee its correctness and avoid inconsistencies. We present a framework for designing and composing semantic Web services at the knowledge level in a language-independent manner. We base our framework on a stack of ontologies that explicitly describes different semantic Web services features, and on the assumption that the semantic Web services are modeled as problem-solving methods (PSMs) that describe the services internal structure.

Composition of web services through genetic programming

Web Services are interfaces that describe a collection of operations that are network-accessible through standardized web protocols. When a required operation is not found, several services can be compounded to get a composite service that performs the desired task. To find this composite service a search process in a, generally, huge search space must be performed. The algorithm that composes the services must select the adequate atomic processes and, also, must choose the correct way to combine them using the different available control structures. In this paper a genetic programming algorithm for web services composition is presented. The algorithm has a context-free grammar to generate the valid structures of the composite services and, also, it includes a method to update the attributes of each node. Moreover, the proposal tries to minimize the number of services, and looks for compositions with the minimum execution path. A full experimental validation with four different repositories with up to 1,090 web services has been done, showing a great performance in all the tests as the algorithm finds a valid solution with a short execution path.

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.

ProDiGen: mining complete, precise and minimal structure process models with a genetic algorithm

Abstract Process discovery techniques automatically extract the real workflow of a process by analyzing the events that are collected and stored in log files. Although in the last years several process discovery algorithms have been presented, none of them guarantees to find complete, precise and simple models for all the given logs. In this paper we address the problem of process discovery through a genetic algorithm with a new fitness function that takes into account both completeness, precision and simplicity. ProDiGen (Process Discovery through a Genetic algorithm) includes new definitions for precision and simplicity, and specific crossover and mutation operators. The proposal has been validated with 39 process models and several noise levels, giving a total of 111 different logs. We have compared our approach with the state of the art algorithms; non-parametric statistical tests show that our algorithm outperforms the other approaches, and that the difference is statistically significant.

An Optimal and Complete Algorithm for Automatic Web Service Composition

The ability of web services to build and integrate loosely-coupled systems has attracted a great deal of attention from researchers in the field of the automatic web service composition. The combination of different web services to build complex systems can be carried out using different control structures to coordinate the execution flow and, therefore, finding the optimal combination of web services represents a non-trivial search effort. Furthermore, the time restrictions together with the growing number of available services complicate further the composition problem. In this paper the authors present an optimal and complete algorithm which finds all valid compositions from the point of view of the semantic input-output message structure matching. Given a request, a service dependency graph which represents a suboptimal solution is dynamically generated. Then, the solution is improved using a backward heuristic search based on the A* algorithm which finds all the possible solutions with different number of services and runpath. Moreover, in order to improve the scalability of our approach, a set of dynamic optimization techniques have been included. The proposal has been validated using eight different repositories from the Web Service Challenge 2008, obtaining all optimal solutions with minimal overhead.

Graph-based semantic annotation for enriching educational content with linked data

In this paper, a new approach to semantic annotation with linked data in the field of document enrichment is presented. This application has been developed in the domain of Education and contrary to traditional semantic annotation, which relates each relevant term of the document with an instance of the ontology, in our approach relevant terms are connected to a (sub)graph of the ontology. Specifically, each relevant term is related to an instance which is expanded to a predefined depth limit, so the term is finally annotated with a (sub)graph. During the expansion process, instances unrelated with the document topics are ruled out, so only relevant and contextualized information is finally included. As result of this process, the document is annotated with a set of interconnected (sub)graphs, and students may access and navigate through these contents to deepen the topics described in the document. This approach has several benefits. From the document enrichment perspective, a set of (sub)graphs, provides a better description, moreover considering the semantic nature of linked data. From the computational perspective, this approach is also more suitable, particularly in the domain of Education. Filtering linked data is computationally expensive, and thus this process cannot be performed in real time. Our approach has been validated in the e-Learning domain and compared with similar approaches that also annotate with linked data.

Learning Analytics Framework for Educational Virtual Worlds

This paper presents a learning analytics framework for 3D educational virtual worlds that focus on discovering learning flows and checking its conformance through process mining techniques. The core of this framework is an Opensim-based virtual world platform, known as OPENET4VE, that is compliant with the IMS Learning Design specification and that has the ability of monitoring and registering the events generated by students and teachers. Based on these event logs, process mining algorithms automatically extract the real learning flow of the course, allowing teachers to introduce changes in the learning flow initially proposed.

Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition

In this paper we present a hybrid approach for automatic composition of web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: 1) generation of the composition graph for a request; 2) computation of the optimal composition that minimizes a single objective QoS function; 3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: 1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and 2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.