Kostas Kolomvatsos

Facing the cold start problem in recommender systems

A recommender system (RS) aims to provide personalized recommendations to users for specific items (e.g., music, books). Popular techniques involve content-based (CB) models and collaborative filtering (CF) approaches. In this paper, we deal with a very important problem in RSs: The cold start problem. This problem is related to recommendations for novel users or new items. In case of new users, the system does not have information about their preferences in order to make recommendations. We propose a model where widely known classification algorithms in combination with similarity techniques and prediction mechanisms provide the necessary means for retrieving recommendations. The proposed approach incorporates classification methods in a pure CF system while the use of demographic data help for the identification of other users with similar behavior. Our experiments show the performance of the proposed system through a large number of experiments. We adopt the widely known dataset provided by the GroupLens research group. We reveal the advantages of the proposed solution by providing satisfactory numerical results in different experimental scenarios.

Assessing dynamic models for high priority waste collection in smart cities

We focus on a system that adopts IoT-enabled waste collection in Smart Cities.We propose the adoption of dynamic routing for waste collection.We propose four models for the collection of high priority waste bins.We assess the performance of the proposed models based on real data.We evaluate the models through qualitative and quantitative metrics. Waste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city.

An Efficient Time Optimized Scheme for Progressive Analytics in Big Data

Big data analytics is the key research subject for future data driven decision making applications. Due to the large amount of data, progressive analytics could provide an efficient way for querying big data clusters. Each cluster contains only a piece of the examined data. Continuous queries over these data sources require intelligent mechanisms to result the final outcome (query response) in the minimum time with the maximum performance. A Query Controller (QC) is responsible to manage continuous/sequential queries and return the final outcome to users or applications. In this paper, we propose a mechanism that can be adopted by the QC. The proposed mechanism is capable of managing partial results retrieved by a number of processors each one responsible for each cluster. Each processor executes a query over a specific cluster of data. Our mechanism adopts two sequential decision making models for handling the incoming partial results. The first model is based on a finite horizon time-optimized model and the second one is based on an infinite horizon optimally scheduled model. We provide mathematical formulations for solving the discussed problem and present simulation results. Through a large number of experiments, we reveal the advantages of the proposed models and give numerical results comparing them with a deterministic model. These results indicate that the proposed models can efficiently reduce the required time for returning the final outcome to the user/application while keeping the quality of the aggregated result at high levels.

Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey

The new era of Web and Internet of Things (IoT) paradigm is being enabled by the proliferation of various devices like RFIDs, sensors, and actuators. Smart devices (devices having significant computational capabilities, transforming them to ‘smart things’) are embedded in the environment to monitor and collect ambient information. In a city, this leads to Smart City frameworks. Intelligent services could be offered on top of such information related to any aspect of humans’ activities. A typical example of services offered in the framework of Smart Cities is IoT-enabled waste management. Waste management involves not only the collection of the waste in the field but also the transport and disposal to the appropriate locations. In this paper, we present a comprehensive and thorough survey of ICT-enabled waste management models. Specifically, we focus on the adoption of smart devices as a key enabling technology in contemporary waste management. We report on the strengths and weaknesses of various models to reveal their characteristics. This survey sets up the basis for delivering new models in the domain as it reveals the needs for defining novel frameworks for waste management.

MNISIKLIS:Indoor Location Based Services for All

MNISIKLIS is an integrated system aiming to provide universal, indoor locationbased services focusing on navigation. This paper presents the overall MNISIKLIS architecture and certain implementation details. In the context of the Design for All approach, the system targets to the support of several types of users, including persons with disabilities as well as elderly, by exploiting multimodal interaction. Moreover, the system implements effi cient path fi nding algorithms and provides advanced user experience through highly personalized services. MNISIKLIS adopts Semantic Web technologies (e.g., ontologies and reasoning methods) for representing an d managing application models. Furthermore, MNISIKLIS exploits modern positioning techniques in order to achieve high quality positioning. The paper discusses the algorithms and the models that accommodate the services provided by the system. Additionally, an analysis of the positioning subsystem, the user interaction subsystem and the peripheral infrastructure is given. Hence, a new paradigm in the area of location-based systems is presented.

Implicit Deadline Calculation for Seller Agent Bargaining in Information Marketplaces

Present and future Web business models involve the trading of information goods. Information marketplaces can be considered as places where users search and retrieve information goods. Such places appear to be very interesting information retrieval models. Furthermore, software agent technology could help users and providers to work in such open environments providing a variety of advantages. Users as well as information providers could be represented by intelligent agents that work autonomously. The representatives of users assume the role of information buyers while the representatives of information sources could be referred to as sellers. In this paper, we examine a scenario where agents representing entities involved in an information marketplace bargain over the prices of information goods. Bargaining originates in game theory (GT). The rationale is that some entities contest to gain as much profit as possible in an open environment. We study the sellerspsila side. Sellers involved in a number of games with buyers, are trying to achieve as greater prices as possible in order to gain more profit from each game. We present a theoretical model of deadline computation for which sellers are participating in the game. Over this time limit it is useless for sellers to continue the game while buyers reject the proposed prices.

An adaptive fuzzy logic system for automated negotiations

The rapid growth of the Web means that humans become increasingly incapable of searching among millions of resources to find and purchase items. Autonomous entities such as agents could help in these situations. Electronic markets (EMs) are virtual sites where these autonomous entities can interact to exchange items and obtain specific returns. In this study, we consider the interactions between buyers and sellers in EMs, where we focus specifically on the buyer side. These interactions can be modeled as finite horizon negotiations. However, the buyer cannot be certain of the characteristics of the seller during negotiations (incomplete knowledge). Thus, to address this uncertainty, we propose a fuzzy logic (FL) system that is responsible for determining the appropriate actions of the buyer during every negotiation round. We also propose an adaptation technique that updates the FL rule base and system membership functions as necessary. Using this approach, the system can respond to even the complex strategies followed by a seller. A seller strategy estimation method is also adopted by the system, which employs the known kernel density estimator (KDE). We provide results for a large number of negotiations and compare our system with previous research in this area. Our results show that the proposed system exhibits good performance in many negotiation scenarios.

Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques

The current form of Web provides numerous product resources available to users. Users can rely on intelligent agents for purchase actions. These actions are taken in specific environments such as Electronic Markets (EMs). In this paper, we study the interaction process between buyers and sellers and focus on the buyer side. Each buyer has the opportunity to interact with a number of sellers trying to buy the most appropriate products. This interaction can be modeled as a finite horizon Bargaining Game (BG). In this game, players have opposite goals concerning the product price. We adopt a number of techniques in the buyer side trying to give the appropriate level of efficiency in the buyer decision process. The buyer uses a prediction mechanism in combination with the use of Fuzzy Logic (FL) theory in order to be able to predict the upcoming seller proposal and, thus, understand the seller pricing policy. Based on this, he/she can adapt his/her behavior when trying to purchase products. The buyer adaptation mechanism produces the belief that the buyer has about the seller pricing policy and a parameter that indicates his/her own pricing policy which yields the buyer offers in the upcoming rounds. Moreover, the buyer is based on FL system that derives the appropriate actions at every round of the BG. Our results show that the combination of Fuzzy Logic (FL) with the above-mentioned techniques provides an efficient decision mechanism in the buyer side that in specific scenarios outperforms an optimal stopping model.

Debugging applications created by a Domain Specific Language: The IPAC case

Nowadays, software developers have created a large number of applications in various research domains of Computer Science. However, not all of them are familiar with the majority of the research domains. Hence, Domain Specific Languages (DSLs) can provide an abstract, concrete description of a domain in terms that can easily be managed by developers. The most important in such cases is the provision of a debugger for debugging the generated software based on a specific DSL. In this paper, we propose and present a simple but efficient debugger created for the needs of the IPAC system. The debugger is able to provide debugging facilities to developers that define applications for autonomous mobile nodes. The debugger can map code lines between the initial application workflow and the final code defined in a known programming language. Finally, we propose a logging server responsible to provide debugging facilities for the IPAC framework. The IPAC system is consisted of a number of middleware services for mobile nodes acting in a network. In this system a number of mobile nodes exchanged messages that are visualized for more efficient manipulation.

Buyer agent decision process based on automatic fuzzy rules generation methods

Software Agents can assume the responsibility of finding and negotiating products on behalf of their owners in an electronic marketplace. In such cases, Fuzzy Logic can provide an efficient reasoning mechanism especially for the buyer side. Agents representing buyers can rely on a fuzzy rule base in order to reason for their next action at every round of the interaction process with sellers. In this paper, we describe a model where the buyer builds its fuzzy knowledge base using algorithms for automatic fuzzy rules generation based on data provided by experts and compare a set of such algorithms. Owing to such algorithms, agent developers spend less time and effort for the definition of the underlying rule base. Moreover, the rule base is efficiently created through the use of the dataset indicating the behaviour of the buyer and, thus, representing its line of actions in the electronic marketplace. In our work, we use such algorithms for the definition of the buyer behaviour and we provide critical insides for every algorithm describing their advantages and disadvantages. Moreover, we present numerical results for every basic parameter of the interaction process, such as the time required for the rule base generation, the Joint Utility of the interaction process or the value of the acceptance degree that each algorithm results.