A5007788082- Advanced Database Systems and Queries
- Data Quality and Management
- Scientific Computing and Data Management
- Data Management and Algorithms
- Semantic Web and Ontologies
- Service-Oriented Architecture and Web Services
- Distributed systems and fault tolerance
- Advanced Data Storage Technologies
- Research Data Management Practices
- Distributed and Parallel Computing Systems
- Software System Performance and Reliability
- Graph Theory and Algorithms
- Business Process Modeling and Analysis
- Cloud Computing and Resource Management
- Healthcare Technology and Patient Monitoring
- Time Series Analysis and Forecasting
- Mobile Agent-Based Network Management
- Target Tracking and Data Fusion in Sensor Networks
- Knowledge Management and Technology
- Data Mining Algorithms and Applications
- Caching and Content Delivery
- Big Data and Business Intelligence
- Peer-to-Peer Network Technologies
- Electronic Health Records Systems
- Logic, Reasoning, and Knowledge
Oracle (United States)
2009-2021
Illinois Institute of Technology
2017
Digital Wave (United States)
1989-2002
University of Stuttgart
1989
HCA Healthcare
1989
American Jewish Committee
1989
IBM Research - Almaden
1989
Ridgeview
1989
Database needs are changing, driven by the Internet and increasing amounts of scientific sensor data. In this article, authors propose research into several important new directions for database management systems.
research-article Free Access Share on Future Directions in DBMS Research - The Laguna Beach Participants Authors: Philip A. Bernstein View Profile , Umeshwar Dayal David J. DeWitt Dieter Gawlick Jim Gray Matthias Jarke Bruce G. Lindsay Peter C. Lockemann Maier Erich Neuhold Andreas Reuter Lawrence Rowe Hans-Jörg Schek Joachim W. Schmidt Michael Schrefl Stonebraker Authors Info & Claims ACM SIGMOD RecordVolume 18Issue 1March 1989 pp 17–26https://doi.org/10.1145/382272.1367994Published:01...
The authors describe an environment designed to support activities such as a purchase order. They propose simple set of services which would be useful for describing and executing activities. In implementation, underlying system provide these activities, much operating provides processes. consists call interface (Create, Bind, Commit, Abort, CompensationBind, Send, Receive) programs can use request services. These are some the important requirements data processing including concurrency,...
Data provenance is essential for debugging query results, auditing data in cloud environments, and explaining outputs of Big analytics. A well-established technique to represent as annotations on instrument queries propagate these produce results annotated with provenance. However, even sophisticated optimizers are often incapable producing efficient execution plans instrumented queries, because their inherent complexity unusual structure. Thus, while instrumentation enables support...
Decision making is an important part of human activities, and a very active area research. In this article, formal framework for decision developed. At the heart ontology process that classifies both kinds data are availab le modes reasoning used to develop situation awareness make decisions. This ontology-driven resolves two outstanding problems in systems: providing theoretical foundation declarative language support. Concerns addressed such as multiple notions time, recording activities...
This paper presents a novel tandem human-machine cognition approach for human-in-the-loop control of complex business-critical and mission-critical systems processes that are monitored by Internet-of-Things (IoT) sensor networks where it is utmost importance to mitigate avoid cognitive overload situations the human operators. The based on decision making supervisory loop situation awareness combined with machine learning technique especially well suited this problem. goal achieve number...
Provenance for transactional updates is critical many applications such as auditing and debugging of transactions. Recently, we have introduced MV-semirings, an extension the semiring provenance model that supports Furthermore, proposed reenactment, a declarative form replay with capture, efficient non-invasive method computing this type provenance. However, approach limited to snapshot isolation (SI) concurrency control protocol while real world apply read committed version (RC-SI) improve...
Decision making is important for many systems and fundamental situation awareness information fusion. When a decision process confronted with new situations, goals kinds of data, it must evolve adapt. Highly optimized processes efficient data structures generally have the disadvantage having little flexibility or adaptability when forms changing goals. Consequently, may only be locally optimal deteriorate over time. The normal approach to conditions manually reconfigure even redevelop...
Database provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions require understanding the current state a database was transactional history. We present MV-semirings, model for queries histories that supports two common multi-version concurrency control protocols: snapshot isolation (SI) read committed (RC-SI). Furthermore, we introduce an approach retroactively capturing using reenactment, novel technique replaying...
John Boyd recognized in the 1960's importance of situation awareness for military operations and introduced notion OODA loop (Observe, Orient, Decide, Act). Today we realize that many applications have to deal with awareness: Customer Relationship Management, Human Capital Supply Chain patient care, power grid management, cloud services as well any IoT (Internet Things) related application; list seems be endless. Situation requires support management data, knowledge, processes, other such...
In this paper, we examine how active database technology developed over the past few years has been put to use solve real world problems. We note had be extended beyond feature set originally identified in early research meet these real-world needs, and discuss why was best suited solving
Big Data applications need a situation aware computing model to manage data, knowledge, and processes in an ever increasing amount, complexity, speed while reacting as efficiently timely possible any evolving situation. We introduce Knowledge Intensive Data-processing System (KIDS) that empowers support awareness. Frameworks such Apache Hadoop YARN can be leveraged for repeated near real-time execution of knowledge intensive applications. KIDS bridges the gap between world low-value data...