博文
Berkeley的数据库研究小组
||
在今天这个嘈杂的、海量的、异质的网络数据源极其丰富的全球大背景下，信息的处理方式会表现出个性化、协同化、知识化、多媒体化等特征，数据库基础理论研究与应用研究意义更加重大。这个研究组在70年代的数据库管理系统（DBMS）的相关研究中就做出了很多突破性的贡献，造就了几十亿美元的关系数据库产业和一批有影响的开源系统。今天这个小组继续引领基础研究，曾经的辉煌依然继续。传承本身就是一股无形力量。［ INGRES and POSTGRES］
抄袭开源软件是可耻的，免费获取，实现后要开源，遵循许可证要求，国内有的研究项目与相关产品有抄袭开源软件源代码现象,有的进行了有意识处理，有关方面要引起注意。
Topics
-
Declarative Networking
Database systems have long used "declarative" languages, in which programmers focus on program outcomes (what) rather than implementation (how.) In recent years, our group has demonstrated that recursive declarative languages and runtime engines are an excellent match for building distributed and networked systems. Our declarative networking approach provides radically simplified, efficient implementations of tasks as diverse as distributed query processing, statistical inference, distributed agreement, and core networking protocols. We have demonstrated that declarative programs a few dozen lines long compete with C++ implementations that are tens of thousands of lines long. Our software includes the P2 system for declarative overlay networks on the Internet, and the DSN system for declarative programming of wireless sensor networks.
-
Data Management for Wireless Sensor Networks and RFID
Berkeley is the multidisciplinary leader in wireless sensor network research. Sensor networks, and related technologies like RFID infrastructures, are by their nature tools for data acquisition and management, and Berkeley's database group has played a key role in this space. We developed the TinyDB sensornet query engine, the first system to provide a high-level language and runtime for tasking entire"clouds" of sensors in a simple way. We designed probabilistic methods for energy-efficient approximation of sensornet queries and distributed triggers, as well as statistical methods to clean noisy data coming from unpredictable RFID readers. The Declarative Sensor Network (DSN) project described above investigates the use of deductive database techniques to programming entire sensornet "stacks", from core networking internals to high-level data management.
-
Probabilistic Data Management
Several real-world applications need to effectively manage large amounts of data that are inherently uncertain, employing sophisticated probabilistic modeling tools to accurately reason about complex correlation/causality patterns in the data. Example applications include sensor-rich, "smart-home" environments and bioinformatics databases, where noisy, uncertain data is the norm and probabilistic models are used, e.g., to infer user activities or reason about protein molecule structures. We are working to redefine the algorithms and architecture of a DBMS to effectively manage uncertainty and probabilistic reasoning as "first-class citizens" of the system. This includes novel techniques for (a) exposing statistical modeling structures and inference algorithms to key DBMS components (e.g., query engine, query optimizer), and (b) supporting a uniform, declarative means for higher-level applications to store, query, and learn from such probabilistic data.
-
Stream Query Processing
Traditional data management has assumed a stored repository of information. Recent years have seen a proliferation of streaming data sources, including sensor networks, financial data feeds, and monitors of networks and software services. Stream data management raises a number of new challenges in adaptively processing multiple queries, managing fault tolerance, dealing with archives, and providing approximate answers in overload situations. Berkeley's database group has been a leader in this area, investigating these issues and more in the context of the Telegraph project for adaptive processing of stream queries, and in the YFilter XML message broker.
- GridDB, a data-centric workflow system for scientific grid computation.
- HiFi, hierarchical stream processing for RFID and other receptor-based networks.
- P2, a declarative dataflow engine for specifying networks.
- DSN, a Declarative Sensor Network system.
- BOOM, Berkeley Orders Of Magnitude.
https://wap.sciencenet.cn/blog-89075-314159.html
上一篇：中国科学院院士——陆汝钤教授
下一篇：Linux可以杀Windows下的U盘病毒100425
