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The NSF Office of Advanced Cyberinfrastructure has recognized the emerging and evolving need for platforms that fully integrate data and computing workflows, and is calling for research to deliver systems that provide a full spectrum of data services and also offer a coherent coupling with computing software. The Digital Environment to Enable Data-driven Science (DEEDS) project has created a cross-domain, self-serve platform for data and computing that supports the entire end-to-end research investigation process. DEEDS offers interactive interfaces to 1) collect, manage, and explore data, 2) define and launch tools, 3) track computational workflows, 4) access toolkits for ad-hoc analytics, and 5) publish and share the investigations with broader research community. All interfaces are available from a single dashboard so that the workflow between data and tools is smooth and intuitive. This paper highlights the DEEDS platform capabilities for providing integrated data and computing services throughout the entire research investigation lifecycle and presents some examples from use cases that contributed to the platform development.

An ever-expanding photovoltaics (PV) community has been publishing enormous amounts of data regarding all aspects of PV technology. The data generated via these studies and reports range from experiments, simulation, and standard tests to policies and economics of PV. These data require organization, systematic storage, and analyses. Previous works have focused on research-specific data environments and repositories. However, a comprehensive discipline-neutral platform for preserving, sharing, and analyzing the data has not been built. Digital Environment for Enabling Data-Driven Science (DEEDS) provides a unique solution to this problem. DEEDS enables a user to create datasets (projects), cases, and tools; and store data which can be structured, compared, and numerically analyzed, all on a single holistic online platform. In this paper, we demonstrate the capabilities of DEEDS using an example research study called the Solar PV Diagnosis. DEEDS platform has the potential to be used by the entire PV community to preserve various PV projects, interpret their performance and reliability, and to facilitate worldwide collaboration.

DataCenterHub is a new solution for preserving, sharing, and discovering data produced by scientific research. Datasets are organized by experiments, with a simple common structure for metadata, file collections, and key parameters. Researchers associate annotations, reports, media, and measurements to each experiment, and interactive viewers interpret data by type and use so that they can be investigated before downloading. Parameters are extracted for discovery of key data otherwise hidden in files. DataCenterHub provides an alternative discipline-neutral solution, with the goal of helping researchers classify and share data for easy discovery and exploration.

Hierarchical video browsing and feature-based video retrieval are two standard methods for accessing video content. Very little research, however, has addressed the benefits of integrating these two methods for more effective and efficient video content access. In this paper, we introduce Insight Video, a video analysis and retrieval system, which joins video content hierarchy, hierarchical browsing and retrieval for efficient video access. We propose several video processing techniques to organize the content hierarchy of the video. We first apply a camera motion classification and key-frame extraction strategy that operates in the compressed domain to extract video features. Then, shot grouping, scene detection and pairwise scene clustering strategies are applied to construct the video content hierarchy. We introduce a video similarity evaluation scheme at different levels (key-frame, shot, group, scene, and video.) By integrating the video content hierarchy and the video similarity evaluation scheme, hierarchical video browsing and retrieval are seamlessly integrated for efficient content access. We construct a progressive video retrieval scheme to refine user queries through the interactions of browsing and retrieval. Experimental results and comparisons of camera motion classification, key-frame extraction, scene detection, and video retrieval are presented to validate the effectiveness and efficiency of the proposed algorithms and the performance of the system. © 2005 IEEE.

The Indiana Center for Database Systems (ICDS) at Purdue University has embarked in an ambitious endeavor to become a premiere world-class database research center. This goal is substantiated by the diversity of its research topics, the large and diverse funding base, and the steady publication trend in top conferences and journals. ICDS was founded with an initial grant from the State of Indiana Corporation of Science and Technology in 1990. Since then it has grown to now have 9 faculty members and about 30 total researchers. This report describes the major research projects underway at ICDS as well as efforts to move research toward practice.

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of video-based applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting low-level visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

Purpose A Web-based analytics system for conducting inhouse evaluations and cross-facility comparisons of alert data generated by smart infusion pumps is described. Summary The Infusion Pump Informatics (IPI) project, a collaborative effort led by research scientists at Purdue University, was launched in 2009 to provide advanced analytics and tools for workflow analyses to assist hospitals in determining the significance of smart-pump alerts and reducing nuisance alerts. The IPI system allows facility-specific analyses of alert patterns and trends, as well as cross-facility comparisons of alert data uploaded by more than 55 participating institutions using different types of smart pumps. Tools accessible through the IPI portal include (1) charts displaying aggregated or breakout data on the top drugs associated with alerts, numbers of alerts per device or care area, and override-to-alert ratios, (2) investigative reports that can be used to characterize and analyze pump-programming errors in a variety of ways (e.g., by drug, by infusion type, by time of day), and (3) “drill-down” workflow analytics enabling users to evaluate alert patterns—both internally and in relation to patterns at other hospitals—in a quick and efficient stepwise fashion. Conclusion The formation of the IPI analytics system to support a community of hospitals has been successful in providing sophisticated tools for member facilities to review, investigate, and efficiently analyze smart-pump alert data, not only within a member facility but also across other member facilities, to further enhance smart pump drug library design.

We outline the design of a recommendation system (MyPYTHIA) implemented as a Web portal. MyPYTHIA's design objectives include evaluating the quality and performance of scientific software on Grid platforms, creating knowledge about which software and computational services should be selected for solving particular problems, selecting parameters of software (or of computational services) based on user-specified computational objectives, providing access to performance data and knowledge bases over the Web and enabling recommendations for targeted application domains. MyPYTHIA uses a combination of statistical analysis, pattern extraction techniques and a database of software performance to map feature-based representations of problem instances to appropriate software. MyPYTHIA's open architecture allows the user to customize it for conducting individual case studies. We describe the architecture as well as several scientific domains of knowledge enabled by such case studies. Copyright © 2002 John Wiley & Sons, Ltd.

The article presents the software architecture and implementation of the problem-solving environment (PSE) PELLPACK for modeling physical objects described by partial differential equations (PDEs). The scope of this PSE is broad, as PELLPACK incorporates many PDE solving systems, and some of these, in turn, include several specific PDE solving methods. Its coverage for 1D, 2D. and 3D elliptic or parabolic problems is quite broad, and it handles some hyperbolic problems, Since a PSE should provided complete support for the problem-solving process, PELLPACK also contains a large amount of code to support graphical user interfaces, analystic tools, user help, domain or mesh partitioning, machine and data selection, visualization, and various other tasks. Its total size is well over 1 million lines code. Its open-ended software architecture consists of several software layers. The top layer is an interactive graphical interface for specifying the PDE model and its solution framework This interface saves that results of the user specification in the form of a very high level PDE language which is an alternative interface to the PELLPACK system. This language also allows a user to specify the PDE problem and its solution framework textually in a natural form. The PELLPACK language preprocessor generates a Fortran control program with the interfaces, calls to specified components and libraries of the PDE solution framework, and functions defining the PDE problem. The PELLPACK program execution is supported by a hign-level tool where the virtual parallel system is defined, where the compilation, loading, and execution are controlled. Finally, the PELLPACK PSE integrates several PDE libraries and PDE systems based on the decomposition of discrete geometric data to map sparse PDE computations to parallel machines. An instance of the system is available as aWeb server(WebPELLPACK) for public use at http://pellpack.cs.purdue.edu.

We describe a newly established research project called SoftLab in the area of computational science and computational engineering. The SoftLab project attempts to link physical laboratory experimentation with computer control and simulation to provide a virtual laboratory for computational science. We describe the overall project objectives and then introduce the three focus projects of SoftLab : Two Chemical Engineering SoftLabs (bioseparation and computational electronics) and a Mechanical Engineering SoftLab (computational mechanics). Preliminary results of our efforts are also described.

We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the query processing engine, "Nile". Nile extends the query processor engine of an object-relational database management system, PREDATOR, to process continuous queries over data streams. Nile supports extended SQL operators that handle sliding-window execution as an approach to restrict the size of the stored state in operators such as join.

To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.

The increased use of video data sets for multimedia-based applications has created a demand for strong video database support, including efficient methods for handling the content-based query and retrieval of video data. Video query processing presents significant research challenges, mainly associated with the size, complexity and unstructured nature of video data. A video query processor must support video operations for search by content and streaming, new query types, and the incorporation of video methods and operators in generating, optimizing and executing query plans. In this paper, we address these query processing issues in two contexts, first as applied to the video data type and then as applied to the stream data type. We first present the query processing functionality of the VDBMS video database management system as a framework designed to support the full range of functionality for video as an abstract data type. We describe two query operators for the video data type which implement the rank-join and stop-after algorithms. As videos may be considered streams of consecutive image frames, video query processing can be expressed as continuous queries over video data streams. The stream data type was therefore introduced into the VDBMS system, and system functionality was extended to support general data streams. From this viewpoint, we present an approach for defining and processing streams, including video, through the query execution engine. We describe the implementation of several algorithms for video query processing expressed as continuous queries over video streams, such as fast forward, region-based blurring and left outer join. We include a description of the window-join algorithm as a core operator for continuous query systems, and discuss shared execution as an optimization approach for stream query processing.

In our project, we are adopting a new approach for handling video data. We view the video as a well-defined data type with its own description, parameters and applicable methods. The system is based on PREDATOR, an open-source object-relational DBMS. PREDATOR uses Shore as the underlying storage manager. Supporting video operations (storing, searching-by-content and streaming) and new query types (query-by-example and multi-feature similarity searching) requires major changes in many of the traditional system components. More specifically, the storage and buffer manager has to deal with huge volumes of data with real-time constraints. Query processing has to consider the video methods and operators in generating, optimizing and executing the query plans.

The focus of this paper is the application and extension of the knowledge discovery in databases process [5] developed in PYTHIA recommender system, to analyze the behavior of a DOE ASCI application/hardware pairs in the context of POEMS project[4]. The POEMS project has built a library of models for modeling scalable architectures like those in the ASCI program. Moreover, it supports detail simulation of a variety of state-of-the-art processors and memory hierarchies and incorporates parallel evaluation of discrete-event simulation. The driver application used is SWEEP3D.

Rapid advances in modern networking technologies and commodity high performance computing systems are leading the field of computing in a new paradigm referred to as network-based computing (NC). This paradigm views a large number of geographically distributed computer resources such as PCs, workstations, Symmetric Multi-processors (SMP) and Massively Parallel Processing (MPP) systems connected through a high speed network as a single meta-computer or computational grid [2]. In this paper, we focus on the Internet (WAN) and Intranet (LAN) based computational grids and their ability to support scalable and robust “deep” computing. We present various implementations of the NC paradigm using commodity and customized software in the context of the existing PELLPACK problem solving environment [8], the ITPACK library as it has been implemented in the PELLPACK system, and a muti-physics application for the design of gas turbine engines [11]. Through this study we attempt to assess the feasibility and efficiency of several NC paradigms for scientific applications utilizing existing middleware.

The new economic realities require the rapid prototyping of manufactured artifacts and rapid solutions to problems with numerous interrelated elements. In the SoftLab project, we investigate the issues involved in the design and implementation of a virtual laboratory that simulates and controls the functionality of a wet/dry prototyping laboratory. The 21st century prototyping laboratory environments will include: i) integrated view of the design, simulation, manufacturing, and training phases of the underlying prototyping process, ii) virtual environments capable of controlling both the experimental (physical) and computational (simulation) prototypes and their interactions, iii) knowledge based systems for handling the increasing amount of knowledge and data a designer must synthesize, compare, trade-off, and optimize, iv) numerical simulation systems that support the different possible models of the underlying physical process. In this paper we present a software framework for a virtual laboratory and report on our experiences in its design and implementation.

Problem Solving Environments (PSEs) are very high level software environments that provide all the facilities needed for handling some class of problems. It is clear that building PSEs is a costly endeavor both in terms of the person-years required and the diversity of knowledge and expertise required. This paper describes the Purdue PSE Kernel (PPK), a software framework designed to assist in the development of PSEs. PPK assumes a fairly general model of PSEs, where PSEs are viewed as a collection of communicating, cooperating entities. The architecture of PPK is designed to provide the infrastructure needed to build application PSEs that adhere to this model. The model is realized in terms of an electronic notebook for user interaction with the PSE, an object manager for storing problem information, and a software bus for supporting communication between components of the PSE.