A group of senior database researchers gathers every few years to assess the state of database research and to point out problem areas that deserve additional focus. This report summarizes the discussion and conclusions of the sixth ad-hoc meeting held May 4-6, 2003 in Lowell, Mass. It observes that information management continues to be a critical component of most complex software systems. It recommends that database researchers increase focus on: integration of text, data, code, and streams; fusion of information from heterogeneous data sources; reasoning about uncertain data; unsupervised data mining for interesting correlations; information privacy; and self-adaptation and repair.
Provenance for transactional updates is critical for many applications such
as auditing and debugging of transactions. Recently, we have introduced
MV-semirings, an extension of the semiring provenance model that supports
updates and transactions. Furthermore, we have proposed reenactment, a
declarative form of replay with provenance capture, as an efficient and
non-invasive method for computing this type of provenance. However, this
approach is limited to the snapshot isolation (SI) concurrency control protocol
while many real world applications apply the read committed version of snapshot
isolation (RC-SI) to improve performance at the cost of consistency. We present
non-trivial extensions of the model and reenactment approach to be able to
compute provenance of RC-SI transactions efficiently. In addition, we develop
techniques for applying reenactment across multiple RC-SI transactions. Our
experiments demonstrate that our implementation in the GProM system supports
efficient re-construction and querying of provenance.Comment: long versions of CIKM pape
The existence of the variety of data models and their associated data processing technologies make data management extremely complex. In this paper, we envision a single Multi-Model DataBase Management Systems (MMDBMS) providing declarative accesses to a variety of data models. We briefly review the history of the evolution of the DBMS technology to derive requirements of MMDBMSs and then we illustrate our ideas of building MMDBMSs satisfying those requirements. Since the relational algebra is not powerful enough to provide a mathematical foundation for MMDBMSs, we promote the category theory as a new theoretical foundation, which is a generalization of the set theory. We also suggest a set of shared data infrastructure services among data models to support "Just-In-Time" multi-model data access autonomously.
Decision making is an important part of human activities, and is a very active area of research. In this article, a formal framework for decision making is developed. At the heart of the framework is an ontology for the decision making process that classifies both the kinds of data that are available for the decision making process and the modes of reasoning that are used to develop situation awareness and to make decisions. This ontology-driven framework resolves two outstanding problems in decision making systems: providing a theoretical foundation and declarative language support. Concerns are addressed such as providing for multiple notions of time, recording the activities used in decision making, and maintaining the provenance of both data and activities. A process model is developed that allows for flexible execution of the activities, including either synchronous or asynchronous processing and for exploring alternative or concurrent branches at any stage of the process. Detailed implementation techniques are presented. An issue tracking system is used as a running example to illustrate the concepts being developed. Use cases for the framework are given in a variety of domains including customer support, healthcare, cloud services, and the Internet of Things.
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