Search

We study the case of scheduling a Contextual Information Process (CIP) over incomplete multivariate contextual data streams coming from sensing devices in Internet of Things (IoT) environments. CIPs like data fusion, concept drift detection, and predictive analytics adopt window-based methods for processing continuous stream queries. CIPs involve the continuous evaluation of functions over contextual attributes (e.g., air pollutants measurements from environmental sensors) possibly incomplete (i.e., containing missing values) thus degrading the quality of the CIP results. We introduce a mechanism, which monitors the quality of the contextual streaming values and then optimally determines the appropriate time to activate a CIP. CIP is optimally delayed in hopes of observing in the near future higher quality of contextual values in terms of validity, freshness and presence. Our time-optimized mechanism activates a CIP when the expected quality is maximized taking also into account the induced cost of delay and an aging framework of freshness over contextual values. We propose two analytical time-based stochastic optimization models and provide extensive sensitivity analysis. We provide a comparative assessment with sliding window-centric models found in the literature and showcase the efficiency of our mechanism on improving the quality of results of a CIP. [ABSTRACT FROM AUTHOR]

A large number of contemporary research efforts focus on incomplete data streams handling. These efforts, usually, focus on the creation and maintenance of top- k lists useful to provide efficient responses to top- k queries. In case of large volumes of data accumulated at high rates the problem becomes more intense as an efficient method for maintaining the top- k list is considered imperative. In this paper, we focus on the behavior of an Observer Entity (OE) responsible to observe the incoming data and initiate the maintenance process of the top-k list . The maintenance process involves the calculation of a score for each object and the update of the top- k list. We adopt the principles of Optimal Stopping Theory (OST) and introduce a scheme that determines, in the appropriate time, when the OE decides to initiate the maintenance process. Due to the incomplete data setting, the high rate of the incoming data and the large volume of data, the maintenance process is initiated only when the OE has the appropriate amount of information for providing a top- k list. In contrast to other research approaches, we do not maintain any additional sets of objects. Instead, we attempt to minimize the number of the necessary processing tasks over the list of objects. We present a mathematical analysis for our scheme and an extensive experimental evaluation. The comparison with other models shows that the proposed model provides efficiency in the list management and minimizes the required time to result the final top- k list. [ABSTRACT FROM AUTHOR]

Abstract: Web business models typically rely on environments where entities, not known in advance, try to negotiate and agree on the purchase of products. Such environments are termed Electronic Markets (EMs). In EMs there are two main groups of entities: the buyers and the sellers. Intelligent agents can play the role of buyers and sellers as delegates of them. Agents, acting autonomously, can guarantee the efficiency in the discovery of items of interest to the buyer. The interaction between buyers and sellers can be modeled as a zero knowledge negotiation. In this paper, we discuss basic characteristics of the negotiation and define a decision support mechanism for sellers. We focus on bilateral single issue negotiations between a buyer and a seller. The proposed decision making mechanism is based on Fuzzy Logic (FL) in order to handle uncertainty in the negotiation process. The seller, at every negotiation round, receives the buyer’s offer and decides her course of actions. In this setting, we consider that no knowledge on the strategies that entities follow is available. The seller uses fuzzy inference rules in order to decide if she is going to accept or reject the offer of the buyer at every round. Compared with other relevant schemes, our approach demonstrates increased efficiency by raising the utility that the seller obtains through negotiations. [Copyright &y& Elsevier]

Abstract: Electronic Marketplaces (EMs) can offer a number of advantages for users searching for products. In EMs, Intelligent Agents (IAs) can undertake the responsibility of representing buyers and sellers and negotiate over the conclusion of purchases. For this purpose, a negotiation is held between IAs. The most important characteristics are the deadline and the pricing strategy. The strategy defines the proposed prices at every round of the negotiation. We focus on the buyer side. We study concurrent negotiations between a buyer and a set of sellers. In this setting, the buyer utilizes a number of threads. Each thread follows a specific strategy and adopts swarm intelligence techniques for achieving the optimal agreement. The Particle Swarm Optimization (PSO) algorithm is adopted by each thread. Our architecture requires no central coordination. In real situations, there is absolutely no knowledge for the characteristics of the involved entities. In this paper, we model such kind of uncertainty through known techniques for estimating the distribution of deadlines and strategies. One of them is the Kernel Density Estimation (KDE) technique. Our experimental results depict the time interval where the agreement is possible and the efficiency of the proposed model. [Copyright &y& Elsevier]