Metagenomics holds enormous promise for discovering novel enzymes and organisms that are biomarkers or causes of processes relevant to disease, industry and the environment. In the last two years we have seen a paradigm shift in metagenomics to the application of broad cross-sectional and longitudinal studies enabled by advances in DNA sequencing and high-performance computing. These technologies now make it possible to broadly assess microbial diversity and function, allowing systematic investigation of the largely unexplored frontier of microbial life. To achieve this aim, the global scientific community must collaborate and agree upon common objectives and data standards to enable comparative research across the Earth’s microbiome. Improvements in comparability of data will facilitate the study of biotechnologically relevant processes such as bioprospecting for new glycoside hydrolases or identifying novel energy sources.

Between July 18th and 24th 2010, 26 leading microbial ecology, computation, bioinformatics and statistics researchers came together in Snowbird, Utah (USA) to discuss the challenge of how to best characterize the microbial world using next-generation sequencing technologies. The meeting was entitled “Terabase Metagenomics” and was sponsored by the Institute for Computing in Science (ICiS) summer 2010 workshop program. The aim of the workshop was to explore the fundamental questions relating to microbial ecology that could be addressed using advances in sequencing potential. Technological advances in next-generation sequencing platforms such as the Illumina HiSeq 2000 can generate in excess of 250 billion base pairs of genetic information in 8 days. Thus, the generation of a trillion base pairs of genetic information is becoming a routine matter. The main outcome from this meeting was the birth of a concept and practical approach to exploring microbial life on earth, the Earth Microbiome Project (EMP). Here we briefly describe the highlights of this meeting and provide an overview of the EMP concept and how it can be applied to exploration of the microbiome of each ecosystem on this planet.

The proliferation of mobile devices and the pervasiveness of wireless technology have provided a major impetus to replicate the network-based service discovery technologies in wireless and mobile networks. However, existing service discovery protocols and delivery mechanisms fall short of accommodating the complexities of the ad-hoc environment. They also place emphasis on device capabilities as services rather than device independent software services, making them unsuitable for m-commerce oriented scenarios. Konark is a service discovery and delivery protocol designed specifically for ad-hoc, peer-to-peer networks, and targeted towards device independent services in general and m-commerce oriented software services in particular. It has two major aspects -service discovery and service delivery. For discovery, Konark uses a completely distributed, peer-to-peer mechanism that provides each device the ability to advertise and discover services in the network. The approach towards service description is XML based. It includes a description template that allows services to be described in a human and software understandable forms. A micro-HTTP server present on each device handles service delivery, which is based on SOAP. Konark provides a framework for connecting isolated services offered by proximal pervasive devices over a wireless medium.

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a b s t r a c tProcess compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log -a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.