5393650
PaDDMAS: parallel and distributed data mining application suite
Discovering complex associations, anomalies and patterns in distributed data sets is gaining popularity in a range of scientific, medical and business applications. Various algorithms are employed to perform data analysis within a domain, and range from statistical to machine learning and AI based techniques. Several issues need to be addressed however to scale such approaches to large data sets, particularly when these are applied to data distributed at various sites. As new analysis techniques are identified, the core tool set must enable easy integration of such analytical components. Similarly, results from an analysis engines must be sharable, to enable storage, visualisation or further analysis of results. We describe the architecture of PaDDMAS, a component based system for developing distributed data mining applications. PaDDMAS provides a tool set for combining pre-developed or custom components using a dataflow approach, with components performing analysis, data extraction or data management and translation. Each component is wrapped as a Java/CORBA object, and has an interface defined in XML. Components can be serial or parallel objects, and may be binary or contain a more complex internal structure. We demonstrate a prototype using a neural network analysis algorithm.
16993770
Service matchmaking with rough sets
With the wide adoption of open grid services architecture (OGSA) and Web services resource framework (WSRF), the grid is emerging as a service-oriented computing infrastructure for engineers and scientists to solve data and computationally intensive problems. It is envisioned that computing resources in a future grid environment will be exposed as services. Service discovery becomes an issue of vital importance for a wider uptake of the grid. This paper presents RSSM, a rough sets based service matchmaking algorithm for service discovery with an aim to tolerate uncertainty in identifying service properties. The evaluation results show that the RSSM algorithm is more effective in service discovery compared with other mechanisms such as UDDI and OWLS.
1974621
Engineering high-performance legacy codes as CORBA components for problem-solving environments
This paper describes techniques used to leverage high-performance legacy codes as CORBA components to a distributed problem-solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high-performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element-based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high-performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a user's desktop computing environment can be extended to a high-performance computing environment using a cluster of workstations or a parallel computer.
45496659
Migrating legacy codes to distributed computing environments: a CORBA approach
Abstract This paper describes techniques used to migrate legacy codes as CORBA components to a distributed problem solving environment (PSE). It presents a CORBA compliant wrapper generator (WG), which can be used to automatically wrap high performance legacy codes in C or Fortran as CORBA components for reuse in the PSE. Using WG, a finite element based computational fluid dynamics (CFD) legacy code has been wrapped as a CORBA component and a PSE for simulating incompressible Navier-Stokes flows has been implemented. A user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a user's desktop computing environment can be extended to a computing environment using a cluster of workstations or a parallel computer.
45224109
Service Matchmaking and Discovery with Rough Sets
The past few years have seen the Grid is evolving as a service-oriented computing infrastructure. It is foreseen that various resources in a Grid environment will be exposed as services for use by a wider user community. Service discovery becomes an issue of vital importance for utilising Grid facilities. This paper presents Rosse, a Rough Sets based Search Engine for service discovery that can deal with uncertainty of service properties when matching service advertisements with service requests. The evaluation results show that Rosse is more effective in service discovery compared with other mechanisms such as UDDI keyword matching and OWL-S matchmaking.
7274224
Wrapping MPI-based legacy codes as Java/CORBA components
Abstract Techniques for wrapping an MPI-based molecular dynamics (MD) simulation code as Java/CORBA components, for use within a distributed component based problem solving environment (CB-PSE), is presented. A legacy code for simulating a Lennard–Jones fluid is first wrapped as a single CORBA object, followed by division of the code into computational sub-units, where each sub-unit is wrapped as a CORBA object containing MPI calls, and run on a cluster of workstations — enabling different MPI implementations to inter-operate. Using a Java implementation, users can submit simulation tasks through a Web based interface, without needing to know implementation details of the legacy code, or the exact interaction between sub-units within the code. We provide performance comparisons of wrapping the entire MD code as a single object versus wrapping sub-units within it, and offer a simple performance model to explain our findings.
1499410
On Prediction by Data Compression
Traditional wisdom has it that the better a theory compresses the learning data concerning some phenomenon under investigation, the better we learn, generalize, and the better the theory predicts unknown data. This belief is vindicated in practice but apparently has not been rigorously proved in a general setting. Making these ideas rigorous involves the length of the shortest effective description of an individual object: its Kolmogorov complexity. In a previous paper we have shown that optimal compression is almost always a best strategy in hypotheses identification (an ideal form of the minimum description length (MDL) principle). Whereas the single best hypothesis does not necessarily give the best prediction, we demonstrate that nonetheless compression is almost always the best strategy in prediction methods in the style of R. Solomonoff. 
 
Paul Vitanyi is also affiliated with the University of Amsterdam. He was supported by NSERC through International Scientific Exchange Award ISE0125663, and by the European Union through NeuroCOLT ESPRIT Working Group Nr. 8556, and by NWO through NFI Project ALADDIN under Contract number NF 62-376. Ming Li was supported in part by NSERC operating grant OGP-046506, ITRC, and a CGAT grant and the Steacie Fellowship. On sabbatical leave from: Department of Computer Science, University of Waterloo; Email: mli@math.uwaterloo.ca.
41006390
Min-energy voltage allocation for tree-structured tasks
We study job scheduling on processors capable of running at variable voltage/speed to minimize energy consumption. Each job in a problem instance is specified by its arrival time and deadline, together with required number of CPU cycles. It is known that the minimum energy schedule for n jobs can be computed in O(n3) time, assuming a convex energy function. We investigate more efficient algorithms for computing the optimal schedule when the job sets have certain special structures. When the time intervals are structured as trees, the minimum energy schedule is shown to have a succinct characterization and is computable in time O(P) where P is the tree’s total path length. We also study an on-line average-rate heuristics AVR and prove that its energy consumption achieves a small constant competitive ratio for nested job sets and for job sets with limited overlap. Some simulation results are also given.
53306617
Human-Machine Collaborative Systems for Microsurgical Applications
Human-machine collaborative systems (HMCSs) are systems that amplify or assist human capabilities during the performance of tasks that require both human judgment and robotic precision. We examine the design and performance of HMCSs in the context of microsurgical procedures such as vitreo-retinal eye surgery. Three specific problems considered are: (1) development of systems tools for describing and implementing HMCSs, (2) segmentation of complex tasks into logical components given sensor traces of human task execution, and (3) measurement and evaluation of HMCS performance. These components can be integrated into a complete workstation with the ability to automatically “parse” traces of user activities into task models, which are loaded into an execution environment to provide the user with assistance using on-line recognition of task states. The major contributions of this work include an XML task graph modeling framework and execution engine, an algorithm for real-time segmentation of user actions using continuous hidden Markov models, and validation techniques for analyzing the performance of HMCSs.
9175149
Generation of Comprehensible Hypotheses from Gene Expression Data
Machine learning techniques have been recognized as powerful tools for the analysis of gene expression data. However, most learning techniques used in class prediction in gene expression analysis during the past years generate black-box models. Although the prediction accuracy of these models could be very well, they provide little insight into the biological facts. This paper holds the recognition that a more reasonable role for machine learning techniques is to generate hypotheses that can be verified or refined by human experts instead of making decisions for human experts. Based on this recognition, a general approach to generate comprehensible hypotheses from gene expression data is described and applied to human acute leukemias as a test case. The results demonstrate the feasibility of using machine learning techniques to help form hypotheses on the relationship between genes and certain diseases.
1558162
Monitoring Abnormal Patterns with Complex Semantics over ICU Data Streams
Monitoring abnormal patterns in data streams is an important research area for many applications. In this paper we present a new approach MAPS(Monitoring Abnormal Patterns over data Streams) to model and identify the abnormal patterns over the massive data streams. Compared with other data streams, ICU streaming data have their own features: pseudo-periodicity and polymorphism. MAPS first extracts patterns from the online arriving data streams and then normalizes them according to their pseudo-periodic semantics. Abnormal patterns will be detected if they are satisfied the predicates defined in the clinician-specifying normal patterns. At last, a real application demonstrates that MAPS is efficient and effective in several important aspects.