Methods for solving Distributed Constraint OptimizationProblems (DCOP) have emerged as key techniques for distributed reasoning. Yet, their application faces significant hurdles in many multiagent domains due to their inefficiency. Preprocessing techniques have successfully been used to speed up algorithms for centralized constraint satisfaction problems. This paper introduces a framework of different preprocessing techniques that are based on dynamic programming and speed up ADOPT, an asynchronous complete and optimal DCOP algorithm. We investigate when preprocessing is useful and which factors influence the resulting speedups in two DCOP domains, namely graph coloring and distributed sensor networks. Our experimental results demonstrate that our preprocessing techniques are fast and can speed up ADOPT by an order of magnitude.

In this paper, we discuss the state of the art and current trends in designing and optimizing ETL workflows. We explain the existing techniques for: (1) constructing a conceptual and a logical model of an ETL workflow, (2) its corresponding physical implementation, and (3) its optimization, illustrated by examples. The discussed techniques are analyzed w.r.t. their advantages, disadvantages, and challenges in the context of metrics such as autonomous behavior, support for quality metrics, and support for ETL activities as user-defined functions. We draw conclusions on still open research and technological issues in the field of ETL. Finally, we propose a theoretical ETL framework for ETL optimization.

Pervasive sensing is set to transform the future of patient care by continuous and intelligent monitoring of patient well-being. In practice, the detection of patient activity patterns over different time resolutions can be a complicated procedure, entailing the utilisation of multi-tier software architectures and processing of large volumes of data. This paper describes a scalable, distributed software architecture that is suitable for managing continuous activity data streams generated from body sensor networks. A novel pattern mining algorithm is applied to pervasive sensing data to obtain a concise, variableresolution representation of frequent activity patterns over time. The identification of such frequent patterns enables the observation of the inherent structure present in a patient's daily activity for analyzing routine behaviour and its deviations.

Taking support from ego-depletion theory, this study examines ego depletion as a mechanism that explains how employees’ organizational citizenship behavior (OCB) leads to antagonistic consequences, i.e., service sabotage. Employees’ positive psychological capital (PsyCap) is considered a moderator. PROCESS macro was used to test all the hypotheses using time-lagged, dyadic data collected from 420 employees and their 112 their supervisors associated with the service industry in China. This study finds that employees’ exhibition of OCB is positively linked to ego depletion, which in turn drives service sabotage behavior. Furthermore, employees’ PsyCap weakens the effect of OCB on employees’ ego depletion. This study highlights the dark side of OCB, the mechanism through which it causes adverse effects, and the moderating effect of PsyCap. It also provides insights to the organizations for managing service sector employees to effectively interact with customers.