Privacy-Enhanced Data Management for Next-Generation e-Commerce

The Pipeline Open Data Standard (PODS) Association develops and advances global pipeline data standards and best practices supporting data management and reporting for the oil and gas industry. This presentation provides an overview of the PODS Association and a detailed overview of the transformed PODS Pipeline Data Model resulting from the PODS Next Generation initiative. The PODS Association’s Next Generation, or Next Gen, initiative is focused on a complete re-design and modernization of the PODS Pipeline Data Model. The re-design of the PODS Pipeline Data Model is driven by PODS Association Strategy objectives as defined in its 2016–2019 Strategic Plan and reflects nearly 20 years of PODS Pipeline Data Model implementation experience and lessons learned. The Next Gen Data Model is designed to be the system of record for pipeline centerlines and pressurized containment assets for the safe transport of product, allowing pipeline operators to: • Achieve greater agility to build and extend the data model, • respond to new business requirements, • interoperate through standard data models and consistent application interface, • share data within and between organizations using well defined data exchange specifications, • optimize performance for management of bulk loading, reroute, inspection data and history. The presentation will introduce the Next Gen Data Model design principles, conceptual, logical and physical structures with a focus on transformational changes from prior versions of the Model. Support for multiple platforms including but not limited to Esri ArcGIS, open source GIS and relational database management systems will be described. Alignment with Esri’s ArcGIS Platform and ArcGIS for Pipeline Referencing (APR) will be a main topic of discussion along with how PODS Next Gen can be leveraged to benefit pipeline integrity, risk assessment, reporting and data maintenance. The end goal of a PODS implementation is a realization of data management efficiency, data transfer and exchange, to make the operation of a pipeline safer and most cost effective.

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Delivering the Next-Generation Research Repository

Advances in Library and Information Science - Research Data Access and Management in Modern Libraries ◽

10.4018/978-1-5225-8437-7.ch007 ◽

2019 ◽

pp. 144-154

Author(s):

Adi Alter ◽

Eddie Neuwirth ◽

Dani Guzman

Keyword(s):

Data Management ◽

Research Output ◽

Scale Up ◽

Research Data ◽

Next Generation ◽

Data Repositories ◽

Institutional Repositories ◽

Research Data Management ◽

Library Research ◽

Multiple Data

Academic libraries are looking for ways to grow their involvement in and scale-up their support for research activities. The successful transition depends to a large extent on the library's ability to systematically manage data, break down information silos and unify workflows across the library, research office and researchers. Data repositories are at the heart of this challenge, yet often institutional repositories are not built to address the needs of modern research data management due to inability to store all research assets, lack of consistent data models, and insufficient workflows. This chapter will present a new approach to research data management that ensures visibility of research output and data, data coherency, and compliance with open access standards. The authors will discuss a ‘Next-Generation Research Repository' that spans multiple data management activities, including automated data capture, metadata enrichment, dissemination, compliance-related workflows, automated publication to scholarly profiles, as well as open integration with the research ecosystem.

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Scalable Data Mining, Archiving, and Big Data Management for the Next Generation Astronomical Telescopes

Big Data ◽

10.4018/978-1-4666-9840-6.ch100 ◽

2016 ◽

pp. 2199-2225

Author(s):

Chris A. Mattmann ◽

Andrew Hart ◽

Luca Cinquini ◽

Joseph Lazio ◽

Shakeh Khudikyan ◽

...

Keyword(s):

Big Data ◽

Data Management ◽

Next Generation ◽

Data Types ◽

Science Data ◽

Very Large Array ◽

Data Formats ◽

Astronomical Instruments ◽

Data Volume ◽

The Difference

Big data as a paradigm focuses on data volume, velocity, and on the number and complexity of various data formats and metadata, a set of information that describes other data types. This is nowhere better seen than in the development of the software to support next generation astronomical instruments including the MeerKAT/KAT-7 Square Kilometre Array (SKA) precursor in South Africa, in the Low Frequency Array (LOFAR) in Europe, in two instruments led in part by the U.S. National Radio Astronomy Observatory (NRAO) with its Expanded Very Large Array (EVLA) in Socorro, NM, and Atacama Large Millimeter Array (ALMA) in Chile, and in other instruments such as the Large Synoptic Survey Telescope (LSST) to be built in northern Chile. This chapter highlights the big data challenges in constructing data management systems for these astronomical instruments, specifically the challenge of integrating legacy science codes, handling data movement and triage, building flexible science data portals and user interfaces, allowing for flexible technology deployment scenarios, and in automatically and rapidly mitigating the difference in science data formats and metadata models. The authors discuss these challenges and then suggest open source solutions to them based on software from the Apache Software Foundation including Apache Object-Oriented Data Technology (OODT), Tika, and Solr. The authors have leveraged these solutions to effectively and expeditiously build many precursor and operational software systems to handle data from these astronomical instruments and to prepare for the coming data deluge from those not constructed yet. Their solutions are not specific to the astronomical domain and they are already applicable to a number of science domains including Earth, planetary, and biomedicine.

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