Two Case Studies of Subsystem Design for General-Purpose CSCW Software Architectures

This paper discusses subsystem design guidelines for the software architecture of general-purpose computer supported cooperative work systems, i.e., systems that are designed to be applicable in various application areas requiring explicit collaboration support. In our opinion, guidelines for subsystem level design are rarely given most guidelines currently given apply to the programming language level. We extract guidelines from a case study of the redesign and extension of an advanced commercial workflow management system and place them into the context of existing software engineering research. The guidelines are then validated against the design decisions made in the construction of a widely used web-based groupware system. Our approach is based on the well-known distinction between essential (logical) and physical architectures. We show how essential architecture design can be based on a direct mapping of abstract functional concepts as found in general-purpose systems to modules in the essential architecture. The essential architecture is next mapped to a physical architecture by applying software clustering and replication to achieve the required distribution and performance characteristics

Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters

Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target tracking in distributed sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest. To this end, we develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Associative access in persistent object stores : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Information Systems at Massey University

Page 276 missing from original copy.The overall aim of the thesis is to study associative access in a Persistent Object Store (POS) providing necessary object storage and retrieval capabilities to an Object Oriented Database System (OODBS) (Delis, Kanitkar & Kollios, 1998 cited in Kirchberg & Tretiakov, 2002). Associative access in an OODBS often includes navigational access to referenced or referencing objects of the object being accessed (Kim. Kim. & Dale. 1989). The thesis reviews several existing approaches proposed to support associative and navigational access in an OODBS. It was found that the existing approaches proposed for associative access could not perform well when queries involve multiple paths or inheritance hierarchies. The thesis studies how associative access can be supported in a POS regardless of paths or inheritance hierarchies involved with a query. The thesis proposes extensions to a model of a POS such that approaches that are proposed for navigational access can be used to support associative access in the extended POS. The extensions include (1) approaches to cluster storage objects in a POS on their storage classes or values of attributes, and (2) approaches to distinguish references between storage objects in a POS based on criteria such as reference types - inheritance and association, storage classes of referenced storage objects or referencing storage objects, and reference names. The thesis implements Matrix-Index Coding (MIC) approach with the extended POS by several coding techniques. The implementation demonstrates that (1) a model of a POS extended by proposed extensions is capable of supporting associative access in an OODBS and (2) the MIC implemented with the extended POS can support a query that requires associative access in an OODBS and involves multiple paths or inheritance hierarchies. The implementation also provides proof of the concepts suggested by Kirchberg & Tretiakov (2002) that (1) the MIC can be made independent from a coding technique, and (2) data compression techniques should be considered as appropriate alternatives to implement the MIC because they could reduce the storage size required

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments