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.

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The Capability Maturity Model Integration (CMMI) is a renowned Software Process Improvement (SPI) framework. Research studies have revealed that CMMI adoption needs a lot of resources in terms of training, funds, and professional workers. However, the software SMEs (SSMEs) have few resources and cannot adopt CMMI. One of the challenges of adopting CMMI is that CMMI tells "What to do?" as requirements to be met, and leaves "How to do?" to the implementers. The software industry especially SSMEs faces difficulties in successfully implementing various process areas (PAs) particularly Configuration Management Process Area (CM-PA). SG-2 (Track and control changes) is one of the important Specific Goals (SGs) required by CMMI to successfully implement CM-PA. As a starting point, we have achieved this SG by implementing its two contributing Specific Practices (SPs). The proposed WFMs were validated through an Expert Panel Review (EPR) process. In addition, a case study approach was used for the evaluation. The results showed that the models are useful, easy to use, supportive in the achievement of SG-2, and applicable to SSMEs. It is worth mentioning that this research work has not only contributed to the implementation studies but also added to the empirical software engineering body of knowledge.

In activity recognition and behaviour profiling studies, wearable inertial sensors are commonly used to monitor the subjects' daily activities. However, the need of carrying the sensing devices in addition to personal belongings may prohibit the widespread use of the technologies. On the other hand, smartphones have become ubiquitous and most smartphones are already equipped with similar inertial sensors. Recent studies have proposed the use of smartphone for quantifying the activity and behaviour of the users. A smartphone based long-term routine profiling system is proposed. To simplify the user interface and facilitate the ubiquitous use of the system, unsupervised and optimized techniques have been developed and integrated into a mobile phone application. By running the application continuously in the background of the phone, the system captures and processes the sensing information to infer the activities of the users, and the results are forwarded to the server for profiling the routines using pattern mining techniques. The proposed system is validated through a study of six users over two weeks. The ability of the proposed system in capturing routine behavior is demonstrated in the results of the study.

Today’s ETL tools provide capabilities to develop custom code as user-defined functions (UDFs) to extend the expressiveness of the standard ETL operators. However, while this allows us to easily add new functionalities, it also comes with the risk that the custom code is not intended to be optimized, e.g., by parallelism, and for this reason, it performs poorly for data-intensive ETL workflows. In this paper we present a novel framework, which allows the ETL developer to choose a design pattern in order to write parallelizable code and generates a configuration for the UDFs to be executed in a distributed environment. This enables ETL developers with minimum expertise in distributed and parallel computing to develop UDFs without taking care of parallelization configurations and complexities. We perform experiments on large-scale datasets based on TPC-DS and BigBench. The results show that our approach significantly reduces the effort of ETL developers and at the same time generates efficient parallel configurations to support complex and data-intensive ETL tasks.