6598903
The next database revolution
Database system architectures are undergoing revolutionary changes. Most importantly, algorithms and data are being unified by integrating programming languages with the database system. This gives an extensible object-relational system where non-procedural relational operators manipulate object sets. Coupled with this, each DBMS is now a web service. This has huge implications for how we structure applications. DBMSs are now object containers. Queues are the first objects to be added. These queues are the basis for transaction processing and workflow applications. Future workflow systems are likely to be built on this core. Data cubes and online analytic processing are now baked into most DBMSs. Beyond that, DBMSs have a framework for data mining and machine learning algorithms. Decision trees, Bayes nets, clustering, and time series analysis are built in; new algorithms can be added. There is a rebirth of column stores for sparse tables and to optimize bandwidth. Text, temporal, and spatial data access methods, along with their probabilistic reasoning have been added to database systems. Allowing approximate and probabilistic answers is essential for many applications. Many believe that XML and xQuery will be the main data structure and access pattern. Database systems must accommodate that perspective. External data increasingly arrives as streams to be compared to historical data; so stream-processing operators are being added to the DBMS. Publish-subscribe systems invert the data-query ratios; incoming data is compared against millions of queries rather than queries searching millions of records. Meanwhile, disk and memory capacities are growing much faster than their bandwidth and latency, so the database systems increasingly use huge main memories and sequential disk access. These changes mandate a much more dynamic query optimization strategy - one that adapts to current conditions and selectivities rather than having a static plan. Intelligence is moving to the periphery of the network. Each disk and each sensor will be a competent database machine. Relational algebra is a convenient way to program these systems. Database systems are now expected to be self-managing, self-healing, and always-up. We researchers and developers have our work cut out for us in delivering all these features.
33551622
The Theory of Sequential Relations
Suppose that one has some system with inputs drawn from a set Z* which produces outputs in some set Lx*. The system is assumed to have sonm state set S. Suppose that all possible experiments are run on the system ill the sense that we can tabulate a relation R of all inpuVoutput pairs which the system can produce. (We will vary the starting state.) A problem of some importance in systems theory (Zadeh and Desoer, 1963) is to synthesize a system which has as its relation R where R has been "given" in advance. In this paper, we study sequential relations which are the relations which arise in this manner when the system is a finite sequential machine. Our main results include synthesis procedures for constructing the sequential machine when the relation is "reasonably given." Also several characterizations of sequential relations are given as well as connections with other families of relations studied in automata theory. This study was stimulated by a lecture given by Professor 5i. A. Aizermann in July 1964 at the University of California, Berkeley. Professor Aizermann described a method for the synthesis of sequential machines by a "question-answer" procedure. Since the technical details of the method were omitted, we derived a method which satisfied the general characteristics mentioncd by Professor Aizernmnn. Recently, the original algorithm has become available (see Tal, 1964). I t turns out that the two methods have very little in common. Our method is • 7, presented as an appendix to this ]oa~dr. , Our method synthesizes a sequential function f by constructing the minimal sequential machine which computes f; this machine has a dis-
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Greetings from a Filesystem User
3204824
Designing a multi-petabyte database for LSST
The 3.2 giga-pixel LSST camera will produce approximately half a petabyte of archive images every month. These data need to be reduced in under a minute to produce real-time transient alerts, and then added to the cumulative catalog for further analysis. The catalog is expected to grow about three hundred terabytes per year. The data volume, the real-time transient alerting requirements of the LSST, and its spatio-temporal aspects require innovative techniques to build an efficient data access system at reasonable cost. As currently envisioned, the system will rely on a database for catalogs and metadata. Several database systems are being evaluated to understand how they perform at these data rates, data volumes, and access patterns. This paper describes the LSST requirements, the challenges they impose, the data access philosophy, results to date from evaluating available database technologies against LSST requirements, and the proposed database architecture to meet the data challenges.
215936012
Evolution of Data Management
Society has made great strides in capturing, storing, managing, analyzing, and visualizing data. These tasks are generically called data management, and this article sketches its evolution through ...
208948503
Third-Generation Database System Manifesto - The Committee for Advanced DBMS Function.
10432009
Increasing the Effectiveness of OS Research
41817845
Why TP-Lite will Dominate the TP Market
40167878
Why do Computer Stop and What Can be About it?
13482322
Triumphs, Sins, and Challenges of Database Benchmarking