Research Directions for Principles of Data Management (Abridged)

Abstract

Research directions for Principles of Data ManagementPDM played a foundational role in the relational database model, with the robust connection between algebraic and calculus-based query languages, the connection between integrity constraints and database design, key insights for the field of query optimization, and the fundamentals of consistent concurrent transactions. This early work included rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. Since the 1990s we have seen an overwhelming increase in both the production of data and the ability to store and access such data. This has led to a phenomenal metamorphosis in the ways that we manage and use data. During this time, we have gone (1) from stand-alone disk-based databases to data that is spread across and linked by the Web, (2) from rigidly structured towards loosely structured data, and (3) from relational data to many different data models (hierarchical, graph-structured, data points, NoSQL, text data, image data, etc.). Research on PDM has developed during that time, too, following, accompanying and influencing this process. It has intensified research on extensions of the relational model (data exchange, incomplete data, probabilistic data, . . . ), on other data models (hierachical, semi-structured, graph, text, . . . ), and on a variety of further data management areas, including knowledge representation and the semantic web, data privacy and security, and data-aware (business) processes. Along the way, the PDM community expanded its cross-fertilization with related areas, to include automata theory, web services, parallel computation, document processing, data structures, scientific workflow, business process management, data-centered dynamic systems, data mining, machine learning, information extraction, etc.Looking forward, three broad areas of data management stand out where principled, mathematical thinking can bring new approaches and much-needed clarity. The first relates to the full lifecycle of so-called "Big Data Analytics", that is, the application of statistical and machine learning techniques to make sense out of, and derive value from, massive volumes of data. The second stems from new forms of data creation and processing, especially as it arises in applications such as web-based commerce, social media applications, and dataaware workflow and business process management. The third, which is just beginning to emerge, is the development of new principles and approaches in support of ethical data management. We briefly illustrate some of the primary ways that these three areas can be supported by the seven PDM research themes that are explored in this report.The overall lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. As documented in numerous sources, so-called "data wrangling" can form 50% to 80% of the labor costs in an analytics investigation. The challenges of data wrangling can be ...

Multi-model Database Management Systems - A Look Forward

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The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning

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Optimization Strategies for Integration Pattern Compositions

“…1. A CRM Material is sent from the CRM system via EDI (more precisely SAP IDOC transport protocol) to an integration process running on SAP Cloud Platform Integration (SAP CPI) 1 . The integration process enriches the message header (MSG.HDR) with additional information based on a document number for reliable messaging (i.e., AppID), which allows redelivery of the message in an exactly-once service quality [40].…”

“…This insight would perhaps not be obvious without the data flow in the model, and leads to questions such as the following: "are there other optimizations that could also be applied, and how can the modeling be supported by this?". Currently these questions cannot be answered, since approaches for verification and static analysis of "realistic data-aware" business and integration processes are missing, as recent surveys on event data [1,16], workflow management [27], and in particular application integration [37] report. Hence, this work aims to fill this gap, based on the following research questions:…”