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Research Interests:
BioComputation: algorithms, machinery and complexity
in physical and biological dynamical systems, continuous algorithms, analog,
adaptive, evolutionaly and chaotic computation.
BioModeling: Circadian thythm networks; Unlearning and drug addiction;
neural networks and attention; cellular development.
BioEngineering: BioInformatics, medical-informatics, mining of
brain signals, mining rhythmic data from microarrays, drug allocation
based on constitutional genetic, predictive engineering tools, Sensors
fusion
Adaptive and active Information systems: adaptive and intereactive
systems; learning algorithms of automatic clustering for datamining, Active
information retrieval systems, active decision support systems.
Dr. Siegelmann is the director of the bio-computation laboratory.
Within this lab, a few fascinating projects run, which relate computational
modeling to biological and cognitive processes. These develop deeper understanding
into biology and at the same time investigate novel computational technologies.
A. Unlearning: It is widely held that learning plays a central role
in addiction. Hallmark characteristic of addiction are compulsive cravings
that are triggered by both internal and external environmental cues. These
cues are stimuli that have been strongly associated with the psychoactive
effects of drug taking as well as drug related behavior, such as drug procurement
and the method of administration. Many treatment approaches for addiction
focus on how these addictive "habits" can be unlearned, on a molecular
as well as a behavioral level.
Machine Learning is an extremely active research area,
and great advances have been made in recent years in both theory and applications.
Yet there have been no direct studies of unlearning, and little attempt
to relate what is known from computational studies of learning to problems
associated with drug addiction.
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Hava T. Siegelmann
Associate Professor
Computer Science
Bio-computation; Complex Systems; Evolving, developing, and Learning
systems; Adaptive and active Information systems; Clustering and Data-mining;
Machine Learning and Neural Networks
By taking a theoretical approach to the problem of addiction,
we do not concern ourselves with representing actual neural structures.
Rather, we seek to understand the general concepts of unlearning and,
through computer simulation, to study how their application can result
in change on a behavioral level. It is from this theoretical perspective
that we are able to produce fundamental new insights into how recovery
and relapse prevention can be enhanced.
B. Simulation Mammalian Molecular Circadian Oscillators
by Dynamic Gene Network: Internal biological rhythms that are entrainable
to the 24-hr light-dark cycle are driven by endogenous oscillators called
circadian clocks. At the molecular level, circadian oscillators are controlled
by autoregulatory feedback loops. The transcriptional-translational feedback
loops involve transcriptional activation/inhibition and translocation
to the nucleus. Several of the genes and proteins involved in the feedback
loops have been well studied in different organisms, such as the mouse
and drosophila, although questions remain to be answered.
Although approximately 10 genes/proteins (Bmal, Clock,
Per1-3, Cry1-2, Rev-erba in mouse) are involved in the regulation of the
core clock generating circadian rhythms, hundreds of genes cycle in different
organs such as the suprachiasmatic nuclei and liver. The relationships
among the core clock genes/proteins and the cycling genes/proteins in
different organs are not yet well understood because of the system's complexity.
Several computational approaches to model biological clocks
have been proposed. However, there has not been a model which is basic
enough to be of help for molecular biologists in the discovery of new
genes or proteins related to circadian rhythms.
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This study implements a gene network to model the circadian
oscillator at the molecular level. The model is simple and can be easily
updated in accordance to new experimental data. While the basic data used
was collected from mice, not many modifications would be required to apply
it to the human biological clock. This network should prove useful in the
discovery of new genes/proteins related to circadian rhythms as well as
in the analysis of drug's effects on the biological clock and in understanding
of the most effective timing for administering various medications.
C. Computation in Gene Networks:
"PHYSICS NEWS UPDATE
The American Institute of Physics Bulletin of Physics News
Number 670 January 22, 2004 by Phillip F. Schewe, Ben Stein, and James
Riordon
Searching for a new way to produce a computational device,
Asa Ben-Hur (Stanford) and Hava Siegelmann (Amherst) have developed a
model which shows that the functioning of a model gene network---genes
acting as a computer "program" and the gene products in a cell
(protein levels) acting as the "memory"---is comparable in expressive
power to the workings of a Turing machine, the generic idealized computer.
They compare a hypothetical analog gene-network computer to standard digital
computers and suggest that chemical reactions can be used to implement
Boolean logic and neural networks. (Chaos, March 2004)"
D. Active Information Systems: We develop a framework
of search in which the agent that has the direct access to data, e.g.,
the engine, assumes an active role. Our framework will be adaptive in
its goals, active in its information gathering and learning, and will
reach an optimal outcome through a dialog between a top-down and a bottom-up
subsystem. We will analyze our search paradigm and prove it to be optimal
in terms of obtaining the fastest and most accurate response in information
retrieval like systems. We will demonstrate another property of our algorithm:
it is an anytime algorithm in the sense that at any point the user can
ask for a response and get an approximated one. By taking more time a
better response is provided.
There is some level of similarity between our framework
and sight. Seeing is not a passive action. In accordance with the top-down
request, our eyes search for the most salient points based on expectation
and attention. The bottom up information gathering process transfers more
than plain pixels upstream to higher brain areas. The executive part at
the frontal lobe may change its criteria based on seen data; for example,
during a military search for bombs, if missiles were detected they can
be added to the parameters of the search. The vision system is an anytime
system in the sense that it always allows for some information and some
suggestion to get into the brain. The recurrent structure of biological
neural networks implies the dialog, and many psychophysical experiments
demonstrate the side effects.
For a detailed description pertaining to Dr. Siegelmann's
published book, please visit: Neural
Networks and Analog Computation: Beyond the Turing Limit, Birkhauser,
Boston, December 1998.
Dr. Sigelmann is a member of the Department
of Computer Science.
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Representative Publications:
1. A. Ben-Hur, H.T. Siegelmann, "Computing with Gene Networks,"
Chaos, January 2004.
2. A. Roitershtein, A. Ben-Hur and H.T. Siegelmann "On
probabilistic analog automata," Theoretical Computer Science, 2004 to appear.
3. A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann
"Random matrix theory for the analysis of the performance of an analog
computer: a scaling theory," Phys. Lett. A. 2004 to appear.
4. A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann,
"Probabilistic analysis of a differential equation for linear programming,"
Journal of Complexity, Volume 19, Issue 4: 474-510 (8.2003)
5. J. P. Neto, H. T. Siegelmann, and J. F. Costa. "Symbolic
processing in neural networks," Journal of the Brazilian Computer Society,
8(3), July 2003.
6. A. Ben-Hur, H.T. Siegelmann and S. Fishman. "A theory
of complexity for continuous time dynamics." Journal of Complexity 18(1)
: 51-86, 2002
7. H.T. Siegelmann, "Neural and Super-Turing Computing,"
Philosophy 2002.
8. A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, "Support
vector clustering," Journal of Machine Learning Research 2:125-137, 2001.
9. Hava T. Siegelmann: Neural Computing. Bulletin of the EATCS
73: 107-130 (2001)
10. S Eldar, H. T. Siegelmann, D. Buzaglo, I. Matter, A. Cohen,
E. Sabo, J. Abrahamson, "Conversion of Laparoscopic Cholecystectomy to
open cholecystectomy in acute cholecystitis: Artificial neural networks improve
the prediction of conversion," World Journal of Surgery. 2001.
11. H.T. Siegelmann A., Ben-Hur, S. Fishman, "Comments
on Attractor Computing," in International Journal of Computing Anticipatory
Systems, D.M. Dubois, ed. 2001 .
12. R. Edwards, H.T. Siegelmann, K. Aziza and L. Glass, "Symbolic
dynamics and computation in model gene networks", Chaos 11(1) (2001).
13. H. Lipson and H.T. Siegelmann, "Geometric Neurons for
Clustering," Neural Computation 12(10), August 2000.
14. D. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar, "Overcoming
Selective Ensemble Averaging: Unsupervised Identification of Event Related Brain
Potentials." IEEE Transactions on Biomedical Engineering, 47(6), June 2000:
822-826.
15. H. Karniely and H.T. Siegelmann, "Sensor Registration
Using Neural Networks," IEEE transactions on Aerospace and Electronic Systems,
36(1), 2000: 85-98.
16. H.T. Siegelmann, "Stochastic Analog Networks and Computational
Complexity," Journal of Complexity, 15(4), 1999: 451-475.
17. H.T. Siegelmann, A. Ben-Hur and S. Fishman, "Computational
Complexity for Continuous Time Dynamics," Physical Review Letters, 83(7),
1999: 1463-1466.
18. H.T. Siegelmann and M. Margenstern, "Nine Neurons Suffice
for Turing Universality," Neural Networks, 12, 1999: 593-600.
19. R. Gavaldà and H.T. Siegelmann, "Discontinuities
in Recurrent Neural Networks," Neural Computation, 11(3), April 1999: 715-745.
20. H.T. Siegelmann and S. Fishman, "Computation by Dynamical
Systems," Physica D 120, 1998: 214-235.
21. A. Galperin, Y. Kimhi, E. Nissan, and H.T. Siegelmann, "FULECON's
Heuristics, their Rationale, and their Representations," The New Review
of Applied Expert Systems 4, 1998: 163-176.
22. H.T. Siegelmann, E. Nissan, and A. Galperin, "A Novel
Neural/Symbolic Hybrid Approach to Heuristically Optimized Fuel Allocation and
Automated Revision of heuristics in Nuclear Engineering," Advances in Engineering
Software, 28(9), 1997: 581-592.
23. J.L. Balcázar, R. Gavaldà, and H.T. Siegelmann,
"Computational Power of Neural Networks: A Characterization in Terms of
Kholmogorov Complexity," IEEE Transactions on Information Theory, 43(4),
July 1997: 1175-1183.
24. H.T. Siegelmann, B.G. Horne, and C.L.Giles, "Computational
Capabilities of Recurrent NARX Neural Networks," IEEE Transaction on Systems,
Man and Cybernetics - part B: Cybernetics, 27(2), 1997: 208-215.
25. E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, "Upgrading
Automation for Nuclear Fuel In-Core Management: From the Symbolic Generation
of Configurations, to the Neural Adaptation of Heuristics," Engineering
with Computers, 13(1), 1997: 1-19.
26. O. Frieder and H.T. Siegelmann, " Document Allocation:
A Genetic Algorithm Approach," IEEE Transactions on Knowledge and Data
Engineering, 9(4), 1997: 640-642.
27. H.T. Siegelmann and C.L. Giles, "The Complexity of
Language Recognition by Neural Networks," Journal of Neurocomputing; Special
Issue "Recurrent Networks for Sequence Processing," Editors: M. Gori,
M. Mozer, A.H. Tsoi, W. Watrous, 15(3-4), 1997: 327-345.
28. H.T. Siegelmann, "On NIL: The Software Constructor
of Neural Networks," Parallel Processing Letters, 6(4), 1996: 575-582.
29. H.T. Siegelmann, "The Simple Dynamics of Super Turing
Theories," Theoretical Computer Science, 168(2)(special issue on UMC),
1996: 461-472.
30. H.T. Siegelmann, "Recurrent Neural Networks and Finite
Automata," Journal of Computational Intelligence, 12(4), 1996: 567-574.
31. J. Kilian and H.T. Siegelmann, "The Dynamic Universality
of Sigmoidal Neural Networks," Information and Computation, 128(1), 1996:
45-56.
32. H.T. Siegelmann, "Analog Computational Power,"
Science, 271(19), January 1996: 373.
33. B. DasGupta, H.T. Siegelmann and E. Sontag, "On the
Complexity of Training Neural Networks with Continuous Activation Functions,"
IEEE Transactions on Neural Networks, 6(6), 1995: 1490-1504.
34. H.T. Siegelmann, "Computation Beyond the Turing Limit,"
Science, 238(28), April 1995: 632-637.
35. H.T. Siegelmann and E.D. Sontag, "Computational Power
of Neural Networks," Journal of Computer System Sciences, 50(1), 1995:
132-150.
36. H.T. Siegelmann and E.D. Sontag, "Analog Computation
via Neural Networks," Theoretical Computer Science, 131, 1994: 331-360.
37. H.T. Siegelmann and E.D. Sontag, "Turing Computability
with Neural Networks," Applied Mathematics Letters, 4(6), 1991: 77-80.
Book Chapters
38. H. T. Siegelmann, "Neural Computing". New Trends
in Computer Science, Gheroge Paul editor, 2003.
39. H.T. Siegelmann, "Neural Automata and Computational
Complexity," in Handbook of Brain Theory and Neural Networks, Michael A.
Arbib (ed.), 2002.
40. H.T. Siegelmann, "Universal Computation and Super-Turing
Capabilities," in Field Guide to Dynamical Recurrent Networks, S.C. Kremer
and J.F. Kolen (eds.), IEEE Press, 2000.
41. H.T. Siegelmann, "Finite vs. Infinite Descriptive Length
in Neural Networks and the Associated Computational Complexity," in Finite
vs. Infinite: Contributions to an Eternal Dilemma, C. Calude and Gh. Paun (eds.),
Springer Verlag, 2000.
42. H.T. Siegelmann, "Neural Automata and Computational
Complexity," in Handbook of Brain Theory and Neural Networks, Michael A.
Arbin (ed.), 2000.
43. H. Lipson and H.T. Siegelmann, "High Order Eigentensors
as Symbolic Rules in Competitive Learning," in Hybrid Neural Symbolic Integration,
S. Wermter and R. Sun (eds.), Springer, 1999.
44. H.T. Siegelmann, "Neural Dynamics with Stochasticity,"
in Adaptive Processing of Sequences and Data Structures, C.L. Giles and M. Gori
(eds.), Springer, 1998: 346-369.
45. H.T. Siegelmann, "Computability with Neural Networks,"
in Lectures in Applied Mathematics, Vol. 32, J. Reneger, M. Shub, and S. Smale
(eds.), American Mathematical Society, 1996: 733-747.
46. H.T. Siegelmann, "Neural Automata," in Shape,
Structures and Pattern Recognition, D. Dori and F. Bruckstein (eds.), World
Scientific, 1995.
47. H.T. Siegelmann, "Towards a Neural Programming Language,"
in Shape, Structures and Pattern Recognition, D. Dori and F. Bruckstein (eds.),
World Scientific, 1995.
48. H.T. Siegelmann, "Recurrent Neural Networks,"
in The 1000th Volume of Lecture Notes in Computer Science: Computer Science
Today, Jan Van Leeuwen (ed.), Springer Verlag, 1995: 29-45.
49. H.T. Siegelmann, "Welcoming the Super-Turing theories,"
in Lecture Notes in Computer Science, Vol. 1012, M. Bartosek, J. Staudek, J.
Wiedermann (eds.), Springer Verlag, 1995: 83-94.
50. H.T. Siegelmann, B.G. Horne, and C.L. Giles, "What
NARX Networks Can Compute," in Lecture Notes in Computer Science: Theory
and Practice of Informatics, Vol. 1012, M. Bartosek, J. Staudek, J. Wiedermann
(eds.), Springer Verlag, 1995: 95-102.
51. B. DasGupta, H.T. Siegelmann, and E. Sontag, "On the
Intractability of Loading Neural Networks," in Theoretical Advances in
Neural Computation and Learning, V.P. Roychowdhury, K.Y. Siu, and A. Orlitsky
(eds.), Kluwer Academic Publishers, 1994: 357-389.
52. H.T. Siegelmann, "On the Computational Power of Probabilistic
and Faulty Neural Networks," in Lecture Notes in Computer Science, Vol.
820: Automata, Languages and Programming, S. Abiteboul and E. Shamir (eds.),
Springer Verlag, 1994: 20-34.
53. H.T. Siegelmann and O. Frieder, "Document Allocation
in Multiprocessor Information Retrieval Systems," in Lecture Notes in Computer
Science, Vol. 759: Advanced Database Concepts and Research Issues, N.R. Adam
and B. Bhargava (eds.), Springer Verlag, November 1993: 289-310.
Refereed Conference Papers (not fully updated)
1. Yanhong Tong and Hava Sieglemann "Simulation mammalian molecular circadian
oscillators by dynamic gene network" Eighth Annual International Conference
on Research in Computational Molecular Biology, March 2004
2. T. Jaakkola and H. Siegelmann. "Active information retrieval."
Advances in Neural Information processing systems 14, 2001.
3. Pedro Rodrigues, José Félix Costa, Hava T.
Siegelmann: Verifying Properties of Neural Networks. IWANN (1) 2001: 158-165
4. Asa Ben-Hur, Hava T. Siegelmann: "Computation in Gene
Networks," MCU 2001: 11-24
5. D. Horn, I. Opher, M. Epstein and H. T. Siegelmann. "Clustering
of Documents using Latent Semantic Analysis" Proceedings of the DAS2000,
Rio - December 2000
6. A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, "A
Support Vector Method for Hierarchical Clustering" Fourteenth Annual Conference
on Neural Information Processing Systems, Denver, Colorado, November 2000.
7. A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik. "A
Support Vector Clustering Method" Proceedings of the 15th International
Conference on Pattern Recognition (ICPR), 728-731, 2000, Barcelona.
8. A. Ben-Hur and H.T. Siegelmann. Computation in gene networks.
in: M. Margenstern and Y. Rogozhin (Eds.): MCU 2001, LNCS 2055, pp. 11-24, 2001.
9. H.T. Siegelmann and A. Roitershtein, "Noisy Neural Computation,"
Proceedings of Thirteenth Annual Conference on Neural Information Processing
Systems, Denver, Colorado, 30 November-2 December, 1999.
10. H.T. Siegelmann, A. Ben-Hur, and S. Fishman, "Computational
Complexity for Continuous Time Dynamics," Proceedings of Third International
Conference on Computing Anticipatory Systems (CASYS'99), Liege, Belgium, 9-14
August, 1999.
11. Hod Lipson, Hava T. Siegelmann: High Order Eigentensors
as Symbolic Rules in Competitive Learning. Hybrid Neural Systems 1998: 286-297
12. H.T. Siegelmann and S. Fishman, 'Attractor Systems and Analog
Computation," Proceedings of the Second International Conference on Knowledge-Based
Intelligent Electronic Systems (KES'98), Adelaide, Australia, 21-23 April, 1998.
13. H. Lipson, Y. Hod, and H.T. Siegelmann, "High-Order
Clustering Metrics for Competitive Learning Neural Networks," Proceedings
of the Israel-Korea Bi-National Conference on New Themes in Computer Aided Geometric
Modeling, Tel-Aviv, Israel, February 18-19, 1998.
14. J.P. Neto, H.T. Siegelmann, and J.F. Costa, "Turing
Universality of neural Nets Revisited," Proceedings of the Sixth International
Conference on Computer Aided Systems Technology (EUROCAST'97). In Franz Pichler
and Roberto Moreno-Diaz (eds.), Lecture Notes in Computer Science (LNCS) 1333,
1997: 3651-366.
15. D.H. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar, "A
Generic Approach for Identification of Event Related Brain Potentials via a
Competitive Neural Network Structure," Proceedings of the International
Conference on Neural Information Proceeding (NIPS), Denver, Colorado, December
1997.
16. Y. Finkelstein and H.T. Siegelmann, "A Stochastic Model
to Study Degenerative Disorders in the Central Nervous System," The Israel
Neurological Association Annual Meeting, Zichron-Yaakov, November 1997.
17. H.T. Siegelmann and S. Fishman, "Computation in Dynamical
Systems," Proceedings of the IEEE International Conference on Systems,
Man, and Cybernetics, October 1997.
18. H.T. Siegelmann, A. Ofri, and H. Guterman, "Applying
Modular Networks and Fuzzy Logic Controllers to Nonlinear flexible Structures,"
Proceedings of the IEEE International Conference on Fuzzy Logic, September 1997.
19. J.P. Neto, H.T. Siegelmann, and J.F. Costa, "Implementation
of Programming Languages with Neural Nets," Proceedings of the First International
Conference on Computing Anticipatory Systems (CASYS'97), HEC, Liege, Belgium,
August 1997.
20. G. Arieli and H.T. Siegelmann, "ANN Approach vs. the
Symbolic Approach in AI," Proceedings of the Thirteenth Israeli Conference
on Artificial Intelligence and Computer Vision (IAICV'97), Tel-Aviv, February
1997.
21. H.T. Siegelmann, 'Recurrent Neural Networks," AMS Proceedings,
Park-City, August 1995.
22. Hava T. Siegelmann: Welcoming the Super Turing Theories.
SOFSEM 1995: 83-94
23. Bill G. Horne, Hava T. Siegelmann, C. Lee Giles: What NARX
Networks Can Compute. SOFSEM 1995: 95-102
24. H.T. Siegelmann, "Recurrent Neural Networks and Finite
Automata," Proceedings of the Twelfth International Conference on Pattern
Recognition , October 1994, Jerusalem.
25. E. Nissan, H.T. Siegelmann, and A. Galperin, "An Integrated
Symbolic and Neural Network Architecture for Machine Learning in the domain
of Nuclear Engineering," Proceedings of the Twelfth International Conference
on Pattern Recognition, October 1994, Jerusalem.
26. E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, "Towards
Full Atomization of the Discovery of Heuristics in a Nuclear Engineering Project:
Integration with a Neural Information Language," Proceedings of the Eight
International Symposium on Methodologies for Intelligent Systems, October 1994,
Charlotte, North Carolina.
27. H.T. Siegelmann, "Neural Programming Language,"
Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-94,
July 31 - August 4, 1994, Seattle, Washington. Menlo PARK (CA): AAAI Press/The
MIT Press, 1994, Vol. 2: 877-882.
28. B. DasGupta, H.T. Siegelmann, and E. Sontag, "On a
Learnability Question Associated to Neural Networks with Continuous Activations,"
Proceedings of the Sixth ACM Workshop on Computational Learning (COLT), New
Brunswick NJ, July 1994.
29. H.T. Siegelmann, "On the Computational Power of Probabilistic
and Faulty Neural Networks," Proceedings of the International Colloquium
on Automata, Languages, and Programming (ICALP), July 1994.
30. J. Kilian and H.T. Siegelmann, "Computability with
the Classical Sigmoid," Proceedings of the Fifth ACM Workshop on Computational
Learning (COLT), Santa Cruz, July 1993: 137-143.
31. Hava T. Siegelmann, Ophir Frieder: Document Allocation In
Multiprocessor Information Retrieval Systems. Advanced Database Systems 1993:
289-310
32. H.T. Siegelmann and E.D. Sontag, "Analog Computation
via Neural Networks," Proceedings of the Second Israel Symposium on Theory
of Computing and Systems (ISTCS), Natanya, Israel, June 1993: 98-107.
33. J.L. Balcázar, R. Gavalda, JH.T. Siegelmann, and
E.D. Sontag, "Some Structural Complexity Aspects of Neural Computation,"
Proceedings of the IEEE Conference on Structure in Complexity Theory, San Diego,
California, May 1993: 253-265.
34. H.T. Siegelmann and E.D. Sontag, "Some Recent Results
on Computing with 'Neural Nets'," Proceedings of the IEEE Conference on
Decision and Control, Tucson, Arizona, December 1992: 1476-1481.
35. H.T. Siegelmann and E.D. Sontag, "On the Computational
Power of Neural Networks," Proceedings of the Fifth ACM Workshop on Computational
Learning Theory (COLT), Pittsburgh, Penn., July 1992: 440-449.
36. H.T. Siegelmann, E.D. Sontag, and C.L. Giles, "The
Complexity of Language Recognition by Neural Networks," Algorithms, Software,
Architecture (J. van Leuwen, ed.), North Holland, Amsterdam, 1992: 329-335.
(Proceedings of the Twelfth IFIP World Computer Congress).
37. H.T. Siegelmann and O. Frieder, "The Allocation of
Documents in Multiprocessor Information Retrieval Systems: An Application of
Genetic Algorithms," Proceedings of the IEEE Conference on Systems, Man,
and Cybernetics, Charlottesville, Virginia, October 1991.
38. O. Frieder and H.T. Siegelmann, "On the Allocation
of Documents in Information Retrieval Systems," Proceedings of the ACM
Fourteenth Conference on Information Retrieval (SIGIR), Chicago, Illinois, October
1991.
39. H.T. Siegelmann and B.R. Badrinath, "Integrating Implicit
Answers with Object-Oriented Queries," Proceedings of the Conference on
Very Large Data Bases, Barcelona, Spain, September 1991.
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