David Camacho

Social big data

Abstract Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.

Intelligent Distributed Computing

Billions of computing elements continuously collect data and elaborate information nowadays. They range from personal computers to high-performance machines, from virtual machines to physical clusters and from smartphones to embedded systems. Most of them are connected, and this number increases continuously. Such an unlimited amount of spread resources offers challenging opportunities for building new kinds of computing overlay, for inferring collective knowledge and for developing emergent applications. In this context, the emergent field of Intelligent Distributed Computing focuses on the development of a new generation of intelligent distributed systems. It faces the challenges of adapting and combining research in the fields of Intelligent Computing and Distributed Computing. Intelligent Computing develops methods and technology ranging from classical artificial intelligence, computational intelligence and multi-agent systems to game theory. The field of Distributed Computing develops methods and technology to build systems that are composed of interacting and collaborating components. The International Symposium on Intelligent Distributed Computing (IDC) has a special interest in (but will not be limited to) novel architectures, systems and methods that facilitate distributed/ parallel/multi-agent biocomputing for solving complex computational and real-life problems. The eighth edition of this conference was held in 2014 in Madrid under the auspicious of Autonomous University of Madrid (www.uam.es). A careful selection from some of the best paper presented at IDC2014 welcomes focused on ‘Intelligent Distributed Computing’ that were selected and invited to be extended for its potential publication at Concurrency and Computation: Practice and Experience journal. The special issue received submissions of original papers on all aspects of IDC ranging from concepts and theoretical developments to advanced technologies and innovative applications; some of the most relevant areas cover by this special issue includes the following: Intelligent Distributed High-performance Architectures; Organization and Management of Intelligent Distributed Systems; Intelligent Distributed Knowledge Representation and Processing; Networked and Distributed Intelligence and Intelligent Distributed Applications and Case Studies. From the received papers, those with the highest quality were selected and finally accepted. In the next section, a short description of final accepted papers is briefly outlined.

A GENETIC GRAPH-BASED APPROACH FOR PARTITIONAL CLUSTERING

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Adaptive K-means algorithm for overlapped graph clustering

The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.

Performance evaluation of ZEUS, Jade, and SkeletonAgent frameworks

Due the growing interest in the field of intelligent agents and multi-agent systems researchers have developed different toolkits. The aim of those toolkits, or frameworks, is to help the designers and engineers to build complex systems based on the agent concept. This paper presents a brief description of some of those frameworks: ZEUS, Jade and SkeletonAgent. These frameworks use their own agent architecture and other facilities like visual programming toolkits, documentation or reusable software libraries to facilitate the definition and development of multi-agent systems. The main aim of this paper is to compare the different frameworks in a common domain: to search news in several electronic newspapers. Because every one of those multi-agent toolkits use different features to build the whole multi-agent system, the behavior of any of those systems is expected to be different. The empirical evaluation measures the request time and the number of retrieved documents for the different systems. Finally, the paper discusses the conclusions for the previous experiments.

Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.

Programming Robosoccer agents by modeling human behavior

The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.

A knowledge-based approach for business process reengineering, SHAMASH

In this paper we present an overview of SHAMASH, a process modelling tool for business process reengineering. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulation and optimisation of them. SHAMASH is a knowledge-based system, and we include a discussion on how knowledge acquisition did take place. Furthermore, we introduce a high level description of the architecture, the conceptual model, and other important modules of the system. q 2002 Elsevier Science B.V. All rights reserved.

Clustering avatars behaviours from virtual worlds interactions

Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.

A new CSP graph-based representation for Ant Colony Optimization

Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.