Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Using schema transformation pathways for data lineage tracing

With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the problems being addressed in data warehousing research. This paper presents a data lineage tracing approach based on schema transformation pathways. Our approach is not limited to one specific data model or query language, and would be useful in any data transformation/integration framework based on sequences of primitive schema transformations

Provenance Explorer: A Tool for Viewing Provenance Trails and Constructing Scientific Publication Packages

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule inference engine

Provenance explorer: Customized provenance views using semantic inferencing

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule-inference engine

bdbms -- A Database Management System for Biological Data

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Sciunits: Reusable Research Objects

Science is conducted collaboratively, often requiring knowledge sharing about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. In this paper, we present the sciunit, a reusable research object in which aggregated content is recomputable. We describe a Git-like client that efficiently creates, stores, and repeats sciunits. We show through analysis that sciunits repeat computational experiments with minimal storage and processing overhead. Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits gaining adoption in the domain of geosciences

PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments

Workflow Provenance: from Modeling to Reporting

Workflow provenance is a crucial part of a workflow system as it enables data lineage analysis, error tracking, workflow monitoring, usage pattern discovery, and so on. Integrating provenance into a workflow system or modifying a workflow system to capture or analyze different provenance information is burdensome, requiring extensive development because provenance mechanisms rely heavily on the modelling, architecture, and design of the workflow system. Various tools and technologies exist for logging events in a software system. Unfortunately, logging tools and technologies are not designed for capturing and analyzing provenance information. Workflow provenance is not only about logging, but also about retrieving workflow related information from logs. In this work, we propose a taxonomy of provenance questions and guided by these questions, we created a workflow programming model 'ProvMod' with a supporting run-time library to provide automated provenance and log analysis for any workflow system. The design and provenance mechanism of ProvMod is based on recommendations from prominent research and is easy to integrate into any workflow system. ProvMod offers Neo4j graph database support to manage semi-structured heterogeneous JSON logs. The log structure is adaptable to any NoSQL technology. For each provenance question in our taxonomy, ProvMod provides the answer with data visualization using Neo4j and the ELK Stack. Besides analyzing performance from various angles, we demonstrate the ease of integration by integrating ProvMod with Apache Taverna and evaluate ProvMod usability by engaging users. Finally, we present two Software Engineering research cases (clone detection and architecture extraction) where our proposed model ProvMod and provenance questions taxonomy can be applied to discover meaningful insights