Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

20000064726 Carnegie-Mellon Univ., School of Computer Science, Pittsburgh, PA USA
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous and Discrete Variables
Davies, Scott; Moore, Andrew; Apr. 2000; 32p; In English
Report No.(s): AD-A377089; CMU-CS-00-119; No Copyright; Avail: CASI; A03, Hardcopy; A01, Microfiche
Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in lowdimensional
continuous spaces. In particular. mixtures of Gaussians can be fitted to data very quickly using an accelerated EM
algorithm that employs multiresolution kd-trees (Moore 1999). In this paper, we propose a kind of Bayesian network in which
low-dimensional mixtures of Gaussians over different subsets of the domain’s variables are combined into a coherent joint probability
model over the entire domain. The network is also capable of modelling complex dependencies between discrete variables
and continuous variables without requiring discretization of the continuous variables. We present efficient heuristic algorithms
for automatically learning these networks from data and perform comparative experiments illustrating how well these networks
model real scientific data and synthetic data. We also briefly discuss some possible improvements to the networks. as well as their
possible application to anomaly detection, classification probabilistic inference, and compression.
DTIC
Bayes Theorem; Machine Learning; Heuristic Methods; Probability Density Functions
20000064913 Technische Univ., Dept. of Mathematics and Computing Science, Eindhoven Netherlands
Response-Time Distribution in a Real-Time Database with Optimistic Concurrency Control and Constant Execution
Times
Sassen, S. A. E.; van der Wal, J.; Mar. 1997; 30p
Report No.(s): PB2000-104847; MEMO-COSOR-97-07; No Copyright; Avail: CASI; A03, Hardcopy; A01, Microfiche
For a real-time shared-memory database with optimistic concurrency control, an approximation for the transaction responsetime
distribution is obtained. The model assumes that transactions arrive at the database according to a Poisson process, that every
transaction uses an equal number of data-items uniformly chosen, and that the multiprogramming level is bounded. The execution
time of all transactions is constant. The behavior of the system is approximated by an M/D/c queue with feedback. The probability
that a transaction must be fed back for a rerun depends on the number of transactions that has committed during its execution.
Numerical experiments, which compare the approximate analysis with a simulation of the database, show that the approximation
of the response-time distribution is quite accurate, even for high system loads.
NTIS
Data Bases; Real Time Operation; Response Time (Computers)
20000064994 George Washington Univ., School of Engineering and Applied Science, Washington, DC USA
Bayesian Aspects of Material Failure, Engineering Reliability, and Software Integrity Final Report, 1 Jan. 1995 - 30 Sep.
1998
Singpurwalla, Nozer D.; Nov. 01, 1998; 5p; In English
Contract(s)/Grant(s): F49620-95-1-0107
Report No.(s): AD-A366897; AFRL-SR-BL-TR-99A-0185; No Copyright; Avail: CASI; A01, Microfiche; A01, Hardcopy
Research on using Bayesian statistical methods and on probabilistic modeling of failure processes is described. Emphasis
is on developing mathematical models for describing the growth of surface and penny shaped cracks in structural materials and
on assessing the integrity of software via a new model for software. Initial work on a paradigm for information fusion is discussed
and issues such as sensor reliability, sensor sabotage, adversarial sensors and sensor parleying are introduced.
DTIC
Failure; Reliability Engineering; Software Engineering
20000066600 Carnegie-Mellon Univ., School of Computer Science, Pittsburgh, PA USA
Spatial Join Selectivity Using Power Laws
Faloutsos, Christos; Seeger, Bernhard; Traina, Agma; Traina, Caetano, Jr; Apr. 2000; 19p; In English; Prepared in cooperation
with Sao Paulo Univ., Dept. of Computer Science, Sao Carlos, Brazil and Universitaet Marburg, Fachbereich Mathematik und
Informatik, Germany. Also referenced as contract no. NSF-DMS98-73442, NSF-IIS98-17496, and NSF-IIS99-1060
Contract(s)/Grant(s): N66001-97-C-8517; NSF-IRI96-25428
Report No.(s): AD-A377144; CMU-CS-00-124; No Copyright; Avail: CASI; A03, Hardcopy; A01, Microfiche
We discovered a surprising law governing the spatial join selectivity across two sets of points. An example of such a spatial
join is ”find the libraries that are within 10 miles of schools”. Our law dictates that the number of such qualifying pairs follows
a power law, whose exponent we call ”pair-count exponent” (PC). We show that this law also holds for self-spatial-joins (”find
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