Enabling Design of Middleware for Massive Scale IOT-based Systems

Recently, the Internet of Things (IoT) technology has rapidly advanced to the stage where it is feasible to discover, locate and identify various smart sensors and devices based on the context, situation, characteristics, and relevancy to query for their data or control actions. Taking things a step further when developing Large Scale Applications requires that two serious issues be overcome. The first issue is to find a solution for data sensing and collection from a massive number of various ubiquitous devices when converging these into the next generation networks. The second important issue is to deal with the “Big Data” that arrive from a very large number of sources. This research emphasizes the need for finding a solution for a large scale data aggregation and delivery. The paper introduces biomimetic design methods for data aggregation in the context of large scale IoT-based systems

A decision support system for the resource constrained project scheduling problem

The paper presents the algorithmic aspects of an interactive decision support system for the resource-constrained project scheduling problem. The system was designed and developed in the context of an international exercise coordinated by IIASA

Online scheduling for a Testing-as-a-Service system

The problem of performing software tests using Testing-as-a-Service cloud environment is considered and formulated as an~online cluster scheduling on parallel machines with total flowtime criterion. A mathematical model is proposed. Several properties of the problem, including solution feasibility and connection to the classic scheduling on parallel machines are discussed. A family of algorithms based on a new priority rule called the Smallest Remaining Load (SRL) is proposed. We prove that algorithms from that family are not competitive relative to each other. Computer experiment using real-life data indicated that the SRL algorithm using the longest job sub-strategy is the best in performance. This algorithm is then compared with the Simulated Annealing metaheuristic. Results indicate that the metaheuristic rarely outperforms the SRL algorithm, obtaining worse results most of the time, which is counter-intuitive for a metaheuristic. Finally, we test the accuracy of prediction of processing times of jobs. The results indicate high (91.4%) accuracy for predicting processing times of test cases and even higher (98.7%) for prediction of remaining load of test suites. Results also show that schedules obtained through prediction are stable (coefficient of variation is 0.2–3.7%) and do not affect most of the algorithms (around 1% difference in flowtime), proving the considered problem is semi-clairvoyant. For the Largest Remaining Load rule, the predicted values tend to perform better than the actual values. The use of predicted values affects the SRL algorithm the most (up to 15% flowtime increase), but it still outperforms other algorithms

The Metamodel of Heritage Preservation for Medical Big Data

© 2018, Springer International Publishing AG. At present the real challenge of Digital Data Preservation concerns methods of keeping all important attributes of the data and preserving their originality. The key is to keep the living part of the data. It is the essence of the Heritage concept. The Heritage is about the concrete data the concept gives the interconnection to other aspects of the reality. Nowadays the physical value and the aspects of items complete the relevance of information. But the question is what is heritage and which parameters defining the artifact or the information as a heritage? The context and the interpretation of data is the answer. The heritage term is defining as the crucial and central part of the presented research. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs