Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

Genetic structure of the Korean black scraper Thamnaconus modestus inferred from microsatellite marker analysis

The Korean black scraper, Thamnaconus modestus, is one of the most economically important maricultural fish species in Korea. However, the annual catch of this fish has been continuously declining over the past several decades. In this study, the genetic diversity and relationships among four wild populations and two hatchery stocks of Korean black scraper were assessed based on 16 microsatellite (MS) markers. A total of 319 different alleles were detected over all loci with an average of 19.94 alleles per locus. The hatchery stocks [mean number of alleles (N A) = 12, allelic richness (A R) = 12, expected heterozygosity (He) = 0.834] showed a slight reduction (P > 0.05) in genetic variability in comparison with wild populations (mean N A = 13.86, A R = 12.35, He = 0.844), suggesting a sufficient level of genetic variation in the hatchery populations. Similarly low levels of inbreeding and significant Hardy–Weinberg equilibrium deviations were detected in both wild and hatchery populations. The genetic subdivision among all six populations was low but significant (overall F ST = 0.008, P < 0.01). Pairwise F ST, a phylogenetic tree, and multidimensional scaling analysis suggested the existence of three geographically structured populations based on different sea basin origins, although the isolation-by-distance model was rejected. This result was corroborated by an analysis of molecular variance. This genetic differentiation may result from the co-effects of various factors, such as historical dispersal, local environment and ocean currents. These three geographical groups can be considered as independent management units. Our results show that MS markers may be suitable not only for the genetic monitoring of hatchery stocks but also for revealing the population structure of Korean black scraper populations. These results will provide critical information for breeding programs, the management of cultured stocks and the conservation of this species.

‘Blind Faith’. An experiment with narrative agency in game design

This paper reports on the current field of narrative-based game design through case study analysis with a particular focus on balancing high narrative agency with low production resources.

Application of simulated annealing to data distribution for all-to-all comparison problems in homogeneous systems

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop's data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.

Prototyping the self-authored video interview : Challenges and opportunities

Self-authored video- where participants are in control of the creation of their own footage- is a means of creating innovative design material and including all members of a family in design activities. This paper describes our adaptation to this process called Self Authored Video Interviews (SAVIs) that we created and prototyped to better understand how families engage with situated technology in the home. We find the methodology produces unique insights into family dynamics in the home, uncovering assumptions and tensions unlikely to be discovered using more conventional methods. The paper outlines a number of challenges and opportunities associated with the methodology, specifically, maximising the value of the insights gathered by appealing to children to champion the cause, and how to counter perceptions of the lingering presence of researchers.

An algorithm for constructing Hjelmslev planes

Projective Hjelmslev planes and affine Hjelmslev planes are generalisations of projective planes and affine planes. We present an algorithm for constructing projective Hjelmslev planes and affine Hjelmslev planes that uses projective planes, affine planes and orthogonal arrays. We show that all 2-uniform projective Hjelmslev planes, and all 2-uniform affine Hjelmslev planes can be constructed in this way. As a corollary it is shown that all $2$-uniform affine Hjelmslev planes are sub-geometries of $2$-uniform projective Hjelmslev planes.

E-learning intervention for STEM education: Developing country case study

This paper reports an innovative and systemic approach to implementing ICT intervention to support enhancement of teaching and learning of STEM subjects in developing countries. The need for adopting ICT was 2 fold: a lack of availability of qualified STEM secondary teachers and a lack of quality teaching and learning resources to assist teachers and students. ICT was seen as being able to impact on both issues. The intervention involved developing sustainable network design including equipment choices, providing high quality e-learning resources and human resource development including teacher training. The intervention has gradually been accepted by teachers, students, and parents and institutionalized as a key feature of the secondary STEM education in the case study country.

Will-making prevalence and patterns in Australia: Keeping it in the family

This article provides evidence of the prevalence of wills and the principles underpinning the intended distribution of estates in Australia. Intentions around wealth transfers and the social norms that underpin them occur in the context of predicted extensive intergenerational transfers from the ageing baby boomer generation, policies of self provision and user pays for care in old age, broader views on what constitutes ‘family’, the increased importance of the not-for-profit sector in the delivery of services, and the related need for philanthropy. A national telephone survey conducted in 2012 with 2,405 respondents aged 18 and over shows that wills are predominantly used to distribute assets to partners and/or equally to immediate descendants. There is little evidence that will makers are recognising a wider group of relationships, obligations and entitlements outside the traditional nuclear family, or that wills are being replaced by other mechanisms of wealth transfer. Only a minority consider bequests to charities as important. These findings reflect current social norms about entitlements to ‘family’ money, a narrow view of what and who constitutes ‘family’, limited obligation for testators to recompense individuals or organisations for care and support provided, and limited commitment to charitable organisations and civil society.

Genetic studies of osteoporosis: A rethink required

It has been 10 years since the seminal paper by Morrison and colleagues reporting the association of alleles of the vitamin D receptor and bone density [1], a paper which arguably kick-started the study of osteoporosis genetics. Since that report there have been literally thousands of osteoporosis genetic studies published, and large numbers of genes have been reported to be associated with the condition [2]. Although some of these reported associations are undoubtedly true, this snow-storm of papers and abstracts has clouded the field to such a great extent that it is very difficult to be certain of the veracity of most genetic associations reported hereto. The field needs to take stock and reconsider the best way forward, taking into account the biology of skeletal development and technological and statistical advances in human genetics, before more effort and money is wasted on continuing a process in which the primary achievement could be said to be a massive paper mountain. I propose in this review that the primary reasons for the paucity of success in osteoporosis genetics has been: •the absence of a major gene effect on bone mineral density (BMD), the most commonly studied bone phenotype; •failure to consider issues such as genetic heterogeneity, gene–environment interaction, and gene–gene interaction; •small sample sizes and over-optimistic data interpretation; and •incomplete assessment of the genetic variation in candidate genes studied.

Thinking like Disney: Supporting the Disney method using ambient feedback based on group performance

The Disney method is a collaborative creativity technique that uses three roles - dreamer, realist and critic - to facilitate the consideration of different perspectives on a topic. Especially for novices it is important to obtain guidance in applying this method. One way is providing groups with a trained moderator. However, feedback about the group’s behavior might interrupt the flow of the idea finding process. We built and evaluated a system that provides ambient feedback to a group about the distribution of their statements among the three roles. Our preliminary field study indicates that groups supported by the system contribute more and roles are used in a more balanced way while the visualization does not disrupt the group work.

A preference-based semantics for CTD reasoning

In [8] the authors developed a logical system based on the definition of a new non-classical connective ⊗ capturing the notion of reparative obligation. The system proved to be appropriate for handling well-known contrary-to-duty paradoxes but no model-theoretic semantics was presented. In this paper we fill the gap and define a suitable possible-world semantics for the system for which we can prove soundness and completeness. The semantics is a preference-based non-normal one extending and generalizing semantics for classical modal logics.