Context-aware data selectivity in Edge Computing (EC) requires nodes to efficiently manage the data collected from Internet of Things (IoT) devices, e.g., sensors, for supporting real-time and data-driven pervasive analytics. Data selectivity at the network edge copes with the challenge of deciding which data should be kept at the edge for future analytics tasks under limited computational and storage resources. Our challenge is to efficiently learn the access patterns of data-driven tasks (analytics) and predict which data arerelevant, thus, being stored in nodes’ local datasets. Task patterns directly indicate which data need to be accessed and processed to support end-users’ applications. We introduce a task workload-aware mechanism which adopts one-class classification to learn and predict the relevant data requested by past tasks. The inherent uncertainty in learning task patterns, identifying inliers and eliminating outliers is handled by introducing a lightweight fuzzy inference estimator that dynamically adapts nodes’ local data filters ensuring accurate data relevance prediction. We analytically describe our mechanism and comprehensively evaluate and compare against baselines and approaches found in the literature showcasing its applicability in pervasive EC.

Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.

Analysts wishing to explore multivariate data spaces, typically issue queries involving selection operators, i.e., range or equality predicates, which define data subspaces of potential interest. Then, they use aggregation functions, the results of which determine a subspace’s interestingness for further exploration and deeper analysis. However, Aggregate Query (AQ) results are scalars and convey limited information and explainability about the queried subspaces for enhanced exploratory analysis. Analysts have no way of identifying how these results are derived or how they change w.r.t query (input) parameter values. We address this shortcoming by aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism based on machine learning. We explain AQ results using functions obtained by a three-fold joint optimization problem which assume the form of explainable piecewise-linear regression functions. A key feature of the proposed solution is that the explanation functions are estimated using past executed queries. These queries provide a coarse grained overview of the underlying aggregate function (generating the AQ results) to be learned. Explanations for future, previously unseen AQs can be computed without accessing the underlying data and can be used to further explore the queried data subspaces, without issuing more queries to the backend analytics engine. We evaluate the explanation accuracy and efficiency through theoretically grounded metrics over real-world and synthetic datasets and query workloads.

We study the performance improvement of Location Based Services through the identification and subsequent use of groups of mobile nodes. In our scheme we exploit the formation of nodes into groups in order to reduce the computation load incurred in back-end systems (e.g., Location Servers) and the associated network overhead. The back-end systems track the position and communicate with the Group Leader (GL). The GL, in turn, passes the received information to the members of the group (e.g., through short-range communications). The formation of mobile groups is validated over time to avoid misinterpreted temporary groupings which could endanger the adoption of the reduced load/overhead scheme. A time scheduling scheme based on the Optimal Stopping Theory assists in the finalization of the group validity. Metrics like group compactness are thoroughly assessed in line with the optimal stopping time scheme to increase confidence on group validity and persistence. Performance assessment reveals significant benefits for the considered location based services system.

Future Web business models involve virtual environments where entities interact in order to sell or buy information goods. Such environments are known as Information Markets (IMs). Intelligent agents are used in IMs for representing buyers or information providers (sellers). We focus on the decisions taken by the buyer in the purchase negotiation process with sellers. We propose a reasoning mechanism on the offers (prices of information goods) issued by sellers based on fuzzy logic. The buyer’s knowledge on the negotiation process is modeled through fuzzy sets. We propose a fuzzy inference engine dealing with the decisions that the buyer takes on each stage of the negotiation process. The outcome of the proposed reasoning method indicates whether the buyer should accept or reject the sellers’ offers. Our findings are very promising for the efficiency of automated transactions undertaken by intelligent agents.