--
Discovery community, focusing on the latest research and applications.
Contributions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to (kdd@gte.com).
E-mail add/delete requests to (kdd-request@gte.com).
Nuggets frequency is approximately weekly.
Back issues of Nuggets, a catalog of S*i*ftware (data mining tools),
and a wealth of other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site, URL http://info.gte.com/~kdd.
-- Gregory Piatetsky-Shapiro (moderator)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
*****************************************************************************
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Mistakes are the portals of discovery.
James Joyce Previous1NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 4 Oct 96 10:27:07 EDT
From: Sal Stolfo (sal@cs.columbia.edu)
Subject: JAM: Java Agents for Meta-learning (Application: Fraud and Intrusion
Detection)
Content-Length: 1195
Readers may be interested in visiting our website for
The JAM Project: Java Agents for Meta-learning.
The JAM Project is building an infrastructure for launching Java-based
learning agents over network-based information systems that then spawn
learned 'classifier agents'. These classifier agents then migrate to
other sites and are combined by 'meta-learning'. The resultant
'meta-classfier agents' can then migrate as well to harvest additional
knowledge from other agents.
The particular application under study using this infrastructure is
targeted to Fraud and Intrusion Detection in Financial Information
Systems. Learning and meta-learning over inherently distributed
databases of transaction information (including fraudulent
transactions) permits remote institutions to collectively learn new
fraud patterns beyond their own experiences in the quest to thwart new
attacks and fraudulent activities.
Our collaborators include the Financial Services Technology
Consortium, a not-for-profit R&D organization whose members include
many of the nation's largest banks and associated vendor community.
High-Performance Knowledge Bases (HPKB) SOL BAA 96-43 DUE 961202 POC David
Gunning, DARPA/ISO, FAX: (703) 696-2203
The goal of the High-Performance Knowledge Base (HPKB) program is to produce
the technology needed to enable system developers to rapidly (within months)
construct large (100K-1M axiom/rule/frame) knowledge-bases that provide
comprehensive coverage of topics of interest, are reusable by multiple
applications with diverse problem-solving strategies, and are maintainable in
rapidly changing environments. It is envisioned that the process for
constructing these large, comprehensive, reusable, and maintainable knowledge
bases would involve three major steps:
#183# Building Foundation Knowledge: creating the foundation knowledge (e.g.,
selecting the knowledge representation scheme, assembling theories of common
knowledge, defining domain-specific terms and concepts) to enable the
construction and population of large, comprehensive knowledge bases for
particular domains of interest -- by selecting, composing, extending,
specializing, and modifying components from a library of reusable ontologies,
common domain theories, and generic problem-solving strategies.
#183# Acquiring Domain Knowledge: constructing and populating a complete
knowledge base -- by using the foundation knowledge to generate
domain-specific knowledge acquisition, data mining, and information extraction
tools -- to enable collaborating teams of domain (non-computer) experts to
easily extend the foundation theories, define additional domain theories and
problem solving strategies, and acquire domain facts to populate a
comprehensive knowledge base covering the domains of interest.
#183# Efficient Problem Solving: enabling efficient problem solving -- either
by providing efficient inference and reasoning procedures to operate on a
complete knowledge base, or by providing tools and techniques to select and
transform knowledge from a complete knowledge base into optimized
problem-solving modules tailored to the unique requirements of an application.
The objective of HPKB is to develop, integrate, and test the technology needed
to enable this process, in a four-year development program, beginning in 1997
and running through September, 2000. The intention is to produce, by the end
of FY00, alternative knowledge-base development environments, which combine
the necessary foundation-building, knowledge-acquisition, and problem-solving
technologies into an integrated development environment, and to use those
environments to build reusable knowledge-base components for multiple DARPA
application projects.
The candidate applications are a set of new and on-going DARPA initiatives;
all of which are building advanced information systems to improve some aspect
of military operations, and all of which need knowledge-rich components to
reason about and understand the battlefield or crisis situation. The
candidate projects are: Dynamic Multi-user Information Fusion (DMIF), Joint
Task Force (JTF) Advanced Technology Demonstration (ATD), Technology
Development for the Joint Force Air Component Commander (JFACC), Advanced
Logistics Program (ALP), Battlefield Awareness Data Dissemination (BADD), and
Information Gathering, Processing and Analyses in Support of Crisis Management
(Project Genoa).
The HPKB program intends to produce multiple knowledge-base development
environments and exercise them to build knowledge-base components for these
applications. The approach for achieving this objective is to: (1) develop
individual foundation-building, knowledge-acquisition, and problem-solving
technologies; (2) integrate those individual technologies into two or more
integrated knowledge-base development environments; (3) develop a set of test
'challenge problems' which reflect the knowledge-base requirements of the
application projects; (4) evaluate the alternative knowledge-base technologies
and integrated development environments against those test problems; and, in
the process (5) produce knowledge-base components for use by the application
projects. These efforts will occur in parallel, with more effort initially
going into tasks #1 and #2 and more effort on task #5 later in the project.
The cornerstone of this approach -- the bridge between technology development
and application -- is the set of 'challenge problems' which will be used to
focus research and development efforts and measure the effectiveness of
alternative technical approaches. Challenge problem test sets will be
developed to be representative of knowledge-base requirements from the
candidate applications. These test sets will be used as the basis for annual
evaluations of the developing technology. Developers will be asked to build
test knowledge bases to satisfy specific test problem specifications. As a
part of the evaluation, the completeness and correctness of the developed
knowledge base will be measured, as well as the time required to build the
knowledge base and the ease of modifying the knowledge base to assimilate new
or changed knowledge. The results of the evaluation will be reported and
discussed in a subsequent conference and used to stimulate sharing and
interchange of promising technical approaches among the research community.
These challenge problems have not been defined in detail, nor have the
specific evaluation metrics or evaluation processes; DARPA is also soliciting,
in this BAA, proposals to define, develop, and maintain the challenge problem
test sets for the HPKB program. Successful proposals in the technology
development and integration category will be expected to propose effort to
participate in the annual challenge problem evaluations. It is estimated that
the evaluation will require development teams to spend one month during each
annual evaluation, using their newly developed tools and techniques to build
test knowledge bases to satisfy the specified challenge problems. The first
evaluation will be held approximately one year after contract award.
After an initial analysis of the candidate applications, two classes of
challenge problems were identified that will be refined to create the specific
challenge problem test sets. The two challenge problems will be based on
developing: (1) comprehensive battlefield knowledge (e.g., terrain
characteristics, force structures, military organizations, troop movements,
military strategy, transportation and logistics operations) to provide
in-depth reasoning to support situation assessment, air campaign planning, and
logistics planning; and (2) general knowledge for information retrieval and
intelligence analysis, involving the development of a broader, more general
knowledge base, suited for information retrieval from a wide variety of
general information sources, such as the Internet.
Through these challenge problems, HPKB technology developers will build and
test knowledge-base tools and use them to incrementally build products for
possible insertion into the application projects. To support this process
DARPA is soliciting, under this BAA, one, two, or three year proposals in the
following two categories: (1) Knowledge-Base Technology Development and
Integration, to develop the individual foundation-building,
knowledge-acquisition, and problem-solving technologies, as well as combine
those technologies into integrated knowledge-base development environments;
and (2) Challenge Problem Development and Evaluation Management, to define,
develop, and maintain the challenge problem test sets, as well as defining and
managing the annual evaluation process and evaluation conferences.
GENERAL INFORMATION.
An industry briefing will be held on 11 OCT 1996. Final proposals are due on
2 DEC 1996. Proposers must submit an original and 10 copies of full proposals
to DARPA/ISO, 3701 North Fairfax Drive, Arlington, VA 22203-1714, (ATTN: BAA
96-43) by 4:00 PM, 2 DEC 1996, in order to be considered.
Proposers must obtain a pamphlet, BAA 96-43 Proposer Information, which
provides further information on areas of interest, the submission, evaluation,
funding processes, and proposal formats. This pamphlet may be obtained by
electronic mail, world-wide web, or mail request to the administrative contact
address given below. Proposals not meeting the format described in the
pamphlet may not be reviewed. This notice, in conjunction with the pamphlet
BAA 96-43 Proposer Information, constitutes the total BAA. No additional
information is available, nor will a formal RFP or other solicitation
regarding this announcement be issued. Requests for same will be disregarded.
The Government reserves the right to select for award all, some, or none of
the proposals received.
All responsible sources capable of satisfying the Government's needs may
submit a proposal which shall be considered. Historically Black Colleges and
Universities (HBCU) and Minority Institutions (MI) are encouraged to submit
proposals and join others in submitting proposals, however, no portion of this
BAA will be set aside for HBCU and MI participation due to the impracticality
of reserving discrete or several areas of research in this area of research.
Awards made under this BAA are subject to the provisions of the Federal
Acquisition Regulation (FAR) Subpart 9.5, Organizational Conflict of Interest.
All offerors and proposed subcontractors must affirmatively state whether
they are supporting any DARPA technical office(s) through an active contract
or subcontract. All affirmations must state which office(s) the offeror
supports, and identify the prime contract number. Affirmations should be
furnished at the time of proposal submission. All facts relevant to the
existence or potential existence of organizational conflicts of interest, as
that term is defined in FAR 9.501, must be disclosed. This disclosure shall
include a description of the action the Contractor has taken, or proposes to
take, to avoid, neutralize or mitigate such conflict.
Evaluation of proposals will be accomplished through a technical review of
each proposal using the following criteria, which are listed in descending
order of relative importance: (1) innovativeness of proposed solutions to meet
program objectives; (2) soundness of technical approach; (3) quality,
quantity, and experience of technical personnel; (4) soundness of program plan
and SOW; (5) cost realism.
The annual budget available to fund proposals from this BAA is approximately
$8M per year. It is estimated that $3-4M per year will be available for
individual technology developments, $2-4M for integrated development
environments, and $1-2M on developing and managing the challenge problem
evaluations. It is expected that individual technology efforts will range
from $200K to $600K per year, and that the integration efforts would range
from $1M to $2M per year, depending on the amount of component technology
development included.
Administrative addresses for this BAA:
All administrative correspondence and questions on this solicitation,
including requests for information on how to submit a proposal to this BAA,
should be directed to one of the administrative addresses below; e-mail or fax
is preferred. DARPA intends to use electronic mail and fax for correspondence
regarding BAA 96-43. Proposals shall not be submitted by fax; any so sent
will be disregarded.
Fax: (703) 696-2203, Addressed to: David Gunning, DARPA/ISO, BAA 96-43;
Electronic Mail: baa96-43@darpa.mil;
Electronic File Retrieval: http://www.darpa.mil/baa/#iso;
Mail: DARPA/ISO, ATTN: BAA 96-43, 3701 N. Fairfax Drive, Arlington, VA
22203-1714.
SPONSOR: Defense Advanced Research Projects Agency (DARPA), Contracts
Management Office (CMO), 3701 North Fairfax Drive, Arlington, VA 22203-1714
SUBFILE: PSE (U.S. GOVERNMENT PROCUREMENTS, SERVICES)
SECTION HEADING: A Research and Development
Previous3NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 14 Oct 1996 12:05:49 -0500 (CDT)
From: Susan Tafolla (stafolla@go.cis.usouthal.edu)
Subject: Automatically Finding Keys in Legacy Databases ?
Seeking advise:
Suppose there were NO domain experts or documentation on a particular
relational database. Is there any software (commericial or otherwise)
that can autonomously identify the keys and foreign keys of the database
tables?
E-mail any advise to: stafolla@cis.usouthal.edu
Thanks!
Previous4NextTop
>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Tue, 15 Oct 1996 10:22:59 -0400
From: gps@gte.com
(Gregory Piatetsky-Shapiro)
Subject: Data Mining and Knowledge Discovery journal
Since there are quite a few special issues of various journals on Data
Mining and Knowledge Discovery, I wanted to remind the research community
of the new journal, soon to be coming out, with the goal to being the
leading journal in the field of Data Mining and Knowledge Discovery
and providing a unifying view of the field.
Here is the journal announcement.
Advances in data gathering, storage, and distribution technologies
have far outpaced computational advances in techniques for analyzing
and understanding data. This created an urgent need for a new
generation of tools and techniques for automated Data Mining and
Knowledge Discovery in Databases (KDD). KDD is a broad area that
integrates methods from several fields including statistics,
databases, AI, machine learning, pattern recognition, machine
discovery, uncertainty modeling, data visualization, high performance
computing, management information systems (MIS), and knowledge-based
systems.
KDD refers to a multi-step process that can be highly interactive and
iterative. It includes data selection/sampling, preprocessing and
transformation for subsequent steps. Data mining algorithms are then
used to discover patterns, clusters and models from data. These
patterns and hypotheses are then rendered in operational forms that
are easy for people to visualize and understand. Data mining is a step
in the overall KDD process. However, most published work has focused
solely on (semi-)automated data mining methods. By including data
mining explicitly in the name of the journal, we hope to emphasize its
role, and build bridges to communities working solely on data mining.
Our goal is to make Data Mining and Knowledge Discovery a flagship
journal publication in the KDD area, providing a unified forum for the
KDD research community, whose publications are currently scattered
among many different journals. The journal will publish
state-of-the-art papers in both the research and practice of KDD,
surveys of important techniques from related fields, and application
papers of general interest. In addition, there will be a special
section including short application reports (1-3 pages), book and
system reviews, and relevant product announcements. The topics of
interest include:
Theory and Foundational Issues in KDD:
Data and knowledge representation for KDD
Modeling of structured, textual, and multimedia data
Uncertainty management in KDD
Metrics for evaluating interestingness and utility of knowledge
Algorithmic complexity, efficiency, and scalability issues in data mining
Limitations of data mining methods
Survey papers of areas and techniques of strong relevance to KDD
including statistics, pattern recognition, optimization, databases,
and other relevant areas.
Data Mining Methods and Algorithms:
Discovery methods based on belief networks, decision trees, genetic
programming, neural networks, inductive logic programming, rough sets,
and other approaches
Algorithms for mining spatial, textual, and other complex data
Incremental discovery methods and re-use of discovered knowledge
Integration of discovery methods
Data structures and query evaluation methods for data mining
Parallel and distributed data mining techniques
Issues and challenges for dealing with massive or small data sets
Fundamental issues from statistics, databases, optimization, and
information processing in general as they relate to problems of
extracting patterns and models from data.
Knowledge Discovery Process:
Data pre-processing for data mining, including data cleaning,
selection, efficient sampling, and data reduction methods.
Evaluating, consolidating, and explaining discovered knowledge
Data and knowledge visualization
Interactive data exploration and discovery
Application Issues:
Application case studies
Data mining systems and tools
Details of successes and failures of KDD
Resource and knowledge discovery on the Internet and WWW
Privacy and security issues
This list of topics is not intended to be exhaustive but an indication
of typical topics of interest. Prospective authors are encouraged to
submit papers on any topics of relevance to knowledge discovery and
data mining.
SUBMISSION AND REVIEW CRITERIA:
We solicit papers on both research and applications. All submitted
papers should be relevant to KDD, clearly written, and be accessible
to readers from other disciplines by including a carefully written
introduction. Submissions will be thoroughly reviewed to ensure they
make a substantial advance either in increasing our understanding of a
fundamental theoretical problem, or provide a strong technological
advance enabling the algorithmic extraction of knowledge from
data. Papers whose primary focus is on significant applications are
strongly encouraged but must clearly address the general underlying
issues and principles, as well as provide details of algorithmic
aspects. Papers whose primary focus is on algorithms and methods must
address issues of complexity, efficiency/feasibility for large data
sets, and clearly state assumptions and limitations of methods
covered. Short application summaries (1-3 pages) are also encouraged
and would be judged on the basis of application significance,
technical innovation, and clarity of presentation.
SUBMISSION INSTRUCTIONS:
To speed up the reviewing process, we strongly encourage electronic
submissions. Instructions for electronic submissions of postscript
files can be obtained via the web at http://www.research.microsoft.com/research/datamine/
Submissions of full papers should be limited to at most 28 pages in
12pt font, 1.5 line-spacing. Electronic submissions will speed the
review process significantly, however due to Kluwer requirements,
authors must also submit hardcopy papers by sending 5 copies to:
Ms. Karen Cullen,
DATA MINING AND KNOWLEDGE DISCOVERY
Editorial Office, Kluwer Academic Publishers,
101 Philip Drive, Norwell, MA 02061
phone 617-871-6600 fax 617-871-6528 email: kcullen@wkap.com
In addition, an e-mail message containing title, abstract, and
keywords must be sent to datamine@microsoft.com
and cc-ed to
kcullen@wkap.com
Please use the electronic template available on the
web. For those with no network access, please e-mail a request to
email: kcullen@wkap.com
Being a publication for a rapidly emerging
field, the journal emphasizes quick dissemination of results and
minimal backlogs in publication time. We plan to review papers and
respond to authors within 3 months of submission. An electronic server
will be made available by Kluwer containing accepted articles and will
be accessible by subscribers to the journal. Authors are encouraged
(whenever appropriate) to make their data available via the journal
web site, allowing papers to have an electronic appendix containing
data and algorithms. The journal will be a quarterly, with a first
volume published in the first half 1997 by Kluwer Academic Publishers.
Editors-in-Chief: Usama M. Fayyad
================ Microsoft Research, USA
The Handbook of Neural Computation is now available for purchase from
Oxford University Press and Institute of Physics Publishing. This
major new resource for the neural computing community offers a wealth
of information on neural network fundamentals, models, hardware and
software implementations, and applications. The handbook includes many
detailed case studies describing successful applications of artificial
neural networks in application areas such as perception and cognition,
engineering, physical sciences, biology and biochemistry, medicine,
economics, finance and business, computer science, and the arts and
humanities.
One of the unique features of this handbook is that it has been
designed to remain up to date: as neural network models, imple-
mentations, and applications continue to develop, the handbook
will keep pace by publishing new articles and revisions to exist-
ing articles. The print edition of the handbook consists of 1,100
A4-size pages published in loose-leaf format, which will be updated
by means of supplements published every six months. The electronic
edition, to be launched in January 1997 but now available for
advance purchase, includes the complete content of the handbook
on CD-ROM, plus integrated access to the latest version of the
handbook's content on the World Wide Web. Hence the handbook
combines inherent updatability with the latest modes of distribution.
The Handbook of Neural Computation is itself part of a larger
project called the Computational Intelligence Library, which
includes companion handbooks in evolutionary and fuzzy computation.
Print Edition: October 1996. 9x12 inches (230x305mm).
Four-post binder expands to accommodate supplements.
1,096 pages, 400 illustrations, ISBN 0-7503-0312-3.
Electronic Edition: January 1997. CD-ROM plus World Wide Web Access.
ISBN 0-7503-0411-1.
Further information, including details of a special introductory
price offer valid until the end of 1996, may be obtained at:
Previous6NextTop
>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Jiawei Han (han@cs.sfu.ca)
Date: Fri, 4 Oct 1996 13:10:43 -0700 (PDT)
Subject: JIIS Special Issue on Data Mining: The final call for papers
Content-Length: 3786
---------------
Call For Papers
---------------
-------------------------------------------------
Journal of Intelligent Information Systems (JIIS)
-------------------------------------------------
----------------------------
Special Issue on Data Mining
----------------------------
As a young, promising research area with broad applications, data mining
and knowledge discovery in databases has attracted great interest in the
research communities of database systems, machine learning, statistics,
high performance computing, information retrieval, data visualization,
and many others. As an example, ACM-SIGMOD'96 Workshop on Research Issues
on Data Mining and Knowledge Discovery (DMKD'96) held in Montreal, Canada
(June 2, 1996) attracted over 100 attendees. Also, KDD'96 conference held
in Portland, Oregon, August 2-4, 1996, received over 200 submissions and
attracted over 500 attendees.
With such overwhelming interest in this area, the Journal of Intelligent
Information Systems (JIIS) is organizing a special issue on Data Mining.
The information on JIIS and instructions to authors are available at:
The journal published a Special Issue on Knowledge Discovery in Databases
in Volume 4, Number 1, January 1995.
We welcome research and applications papers addressing the following issues
to be submitted to this special issue.
1. Foundations, principles and methodologies of data mining, including
Data mining methods and techniques
Efficiency and scalability of KDD algorithms
Mining different kinds of knowledge from data
Integration of deductive and inductive techniques
Statistics, probability and uncertainty in data mining
Maintenance of mined knowledge and knowledge-base construction
Knowledge evolution through learning
Methods for knowledge discovery in advanced database systems (including
object-oriented, deductive, spatial, temporal, textual, multimedia,
heterogeneous, transaction, and active databases, and global
information systems)
2. Systems and implementations for data mining, including
Knowledge discovery systems, implementations, and performance
Languages and interfaces for knowledge discovery in databases
Interactive data mining and knowledge visualization
Integrated discovery systems
Integration of data mining and data warehousing
Systems, implementations, and performance for knowledge discovery
in advanced database systems
3. Knowledge discovery applications, including
Successful knowledge discovery application examples in industry,
administration, business, and science or engineering
New application challenges and requirements for data mining
(e.g., science, engineering, education)
The inadequacy of current knowledge discovery mechanisms
Influence of data mining to the advances of database systems
Security and social impact of data mining
IMPORTANT DATES
Submissions Due: November 1, 1996
Review Notice: January 31, 1997
Final Version due: March 15, 1997
Five hard copies of the paper, with the length limited to 20 pages,
should be submitted by November 1, 1996 to
Dr. Jiawei Han
School of Computing Science
Simon Fraser University
Burnaby, B.C.
Canada, V5A 1S6
han@cs.sfu.ca
JIIS Special Issue Guest Co-Editors
Jiawei Han, Simon Fraser University, Canada (han@cs.sfu.ca).
Laks V.S. Lakshmanan, Concordia University, Canada (laks@cs.concordia.ca).
Raymond Ng, University of British Columbia, Canada (rng@cs.ubc.ca).
Previous7NextTop
>~~~Siftware:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 11 Oct 1996 10:10:41 +0100
From: Nathalia+Gennady Andrienko (gennady@nathan.gmd.de)
Organization: GMD
Subject: Siftware: IRIS: knowledge-based visual data exploration system in WWW
Content-Length: 981
Siftware: IRIS: knowledge-based visual data exploration system in WWW
*URL: http://allanon.gmd.de/and/and.html
*Description: The system provides an intelligent assistance in visual
data exploration by the means of automatical generation of data
presentation on maps.
*Discovery tasks: Geographic Discovery, Visualization
*Comments: The system works in client-server mode. Client part is
implemented
in Java language and runs under Netscape on different platforms.
*Platform(s): Windows 95, Unix (SunOS or Solaris), Mac
There exists a local version of the system for Windows 3.1 (available
from our homepage)
*Contact:
*Status: Research Prototype
*Source of information: see our homepage
*Updated: 1996-10-11 by Dr. Gennady L.Andrienko, e-mail
gennady@nathan.gmd.de
Previous8NextTop
>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: 'Senator, Ted' (senatort@nasd.com)
Subject: KDD Positions at NASD
Date: Fri, 11 Oct 96 11:00:00 PDT
Content-Length: 2772
The National Association of Securities Dealers, Inc. (NASD), through it s
subsidiaries the Nasdaq Stock Market and NASD Regulation, Inc., develops,
operates, and regulates liquid, efficient and fair securities markets for
the ultimate benefit and protection of the investor. NASD Regulation s
Office of Technology Services is hiring for several positions to augment our
core capability in Knowledge Discovery and Data Mining (KDD). More
information about NASD, Nasdaq and NASD Regulation are available at http://www.nasd.com,http://www.nasdaq.com
and http://www.nasdr.com,
respectively.
The Nasdaq Stock Market lists over 5,120 domestic and foreign companies,
more than any other stock market in the world. Nasdaq's share volume reached
101.2 billion shares in 1995 and dollar volume reached $2.39 trillion. In
1995, Nasdaq share volume surpassed that of all other U.S. stock markets.
What distinguishes Nasdaq is its use of computers and a vast
telecommunications network to create an electronic trading system that
allows market participants to meet over the computer rather than face to
face. Our systems use Tandem, Sequent and Sun hosts, with Sun and PC
workstations. NASD/Nasdaq/NASD Regulation employ over 400 technical staff
who design, develop, integrate, test, maintain and operate our trading,
regulatory, and administrative systems.
We are interested in experienced KDD applications developers as well as
recent graduates with backgrounds in machine learning, statistics,
visualization, and database technology. Strong hands-on programming skills
(C/C++, SQL, and/or Java) are required. Knowledge of or interest in the
capital markets is a plus. We work closely with business experts and users
to apply leading edge technology as components of innovative systems to
continually enhance our position as the world s leading regulator of capital
markets.
These positions are located in our Rockville, MD facility outside
Washington, DC. We offer a competitive compensation and flexible benefits
package. NASD/Nasdaq/NASD Regulation are Equal Opportunity/Affirmative
Action Employers and are committed to diversity in our workforce. NASD is
also a drug-free and smoke-free workplace. Only those candidates selected
for an interview will be contacted.
If you are interested, please contact Ted Senator at senatort@nasd.com
for
further information, or forward your resume to Miguel Bustillo, National
Association of Securities Dealers, 15201 Diamondback Dr., Rockville, MD
20850-3389.
Previous9NextTop
>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: 'David Leake' (leake@cs.indiana.edu)
Subject: CFP for ICCBR-97: 2nd Int'l Conf on Case-Based Reasoning
To: ai-cbr@mailbase.ac.uk,
ml@ics.uci.edu,
kaw@swi.psy.uva.nl,
kdd@gte.com,
nl-kr@cs.rochester.edu
Date: Sun, 6 Oct 1996 22:17:48 -0500 (EST)
Content-Length: 10115
Call for Papers
ICCBR-97
--------
Second International Conference on Case-Based Reasoning
-------------------------------------------------------
Providence, Rhode Island, July 25-27, 1997
In 1995, the first International Conference on Case-Based Reasoning
(ICCBR-95) was held in Sesimbra, Portugal, as the start of a biennial
series. ICCBR-97, the Second International Conference on Case-Based
Reasoning, will be held at Brown University in Providence, Rhode Island,
on July 25-27, immediately prior to AAAI-97 and IAAI-97.
The program of ICCBR-97 will include research and applications both in
traditional CBR areas and in other AI areas with strong ties to CBR. The
three-day conference will feature invited talks, paper and poster sessions,
and panels presenting both mature work and new ideas from research and
applications. The conference aims to achieve a vibrant interchange between
researchers and practitioners with different perspectives on fundamentally
related issues, to examine and advance the state of the art in case-based
reasoning and related fields.
Submission areas
----------------
Ideas from case-based reasoning are having an impact in many areas. ICCBR-97
welcomes submissions both in traditional CBR areas and in other artificial
intelligence and cognitive science areas strongly related to case-based
reasoning. Topics of interest include (but are not limited to):
* Case representation, indexing and retrieval, similarity assessment,
case adaptation, and analogical reasoning
* Cognitive models based on CBR
* Case-based and instance-based learning, index learning, and integrating
CBR with other learning methods
* Case-based reasoning and related approaches for task areas such as
education, design, and medicine
* System architectures, integration of CBR with other AI methods,
comparisons to other approaches, and issues in evaluating CBR systems
* AI methods and systems for adaptation of knowledge for reuse, corporate
memories and decision support, knowledge management, intelligent
information retrieval, and networked information discovery and
retrieval
* Novel application areas for case-based techniques, deployed
applications with significant impact, and lessons learned from
application development
Papers may be accepted for presentation as talks, as part of a panel, or as
posters. All accepted papers will appear in the proceedings.
Review Criteria
---------------
Submissions must be identified as either research or applications papers and
will be reviewed using criteria appropriate to their category. Review
criteria for research papers will include scientific significance,
originality, technical quality, and clarity. Review criteria for
applications papers will include significance as applications, impact of the
results and potential to lead to more powerful technology, technical
quality, and clarity.
Important dates
---------------
Submission deadline: 25 February 1997
Notification of acceptance: 1 April 1997
Camera ready copy and author registration due: 29 April 1997
Conference: 25-27 July 1997
Submission Procedure
--------------------
Authors must submit both a full paper and an 'electronic title page.' Full
papers may be submitted in either of two ways:
* Electronically, as a UNIX-printable postscript file, plus one hard
copy. This is the preferred mode of submission.
* In hard copy only, sending five copies.
Authors submitting electronically are responsible for assuring that their
files are UNIX-printable and for performing the electronic submission on or
before February 25. They must also airmail a single hard copy of the
electronic submission to the nearest co-chair as soon as possible after the
electronic submission. Full instructions for electronic submissions will be
posted to http://www.iccbr.org/iccbr-97.html
by January 1, 1997.
Authors submitting only hard copy must send all five hard copies to either
co-chair to arrive on or before February 25. Double-sided copies are
strongly encouraged.
No submissions by fax or submissions arriving after the deadlines will be
accepted.
In addition, authors must submit an electronic version of their title pages
by February 25. Full instructions for electronic title pages will be posted
to http://www.iccbr.org/iccbr-97.html
by January 1, 1997.
Submission Format
-----------------
Submitted papers must begin with a cover page containing paper title,
authors' names, affiliations, postal addresses, electronic mail addresses,
telephone and fax numbers for all authors, and a brief abstract. The cover
page must also specify whether the submission is to be reviewed as a
research paper or applications paper. Unless the title page designates
another contact person, all correspondence will be sent to the first author.
Submissions should be in a 12-point font and double-spaced on 8.5' x 11' or
A4 paper with at least 1 inch margins on all sides. Submissions may be a
maximum of 15 pages long, including cover page, figures and references.
Submissions that do not adhere to these requirements will not be reviewed.
Papers that will be submitted or have been submitted to other conferences
must state this fact on the title page. If a paper will appear at another
conference, it must be withdrawn from ICCBR-97 before March 18th. Papers
that violate these requirements are subject to rejection without review.
Note that this restriction does not apply to papers appearing in the
proceedings of specialized workshops; papers may appear both in those
proceedings and ICCBR. All authors of accepted papers must transfer their
copyrights.
Camera-Ready Copy and Author Registration Policy
------------------------------------------------
Authors of acceped papers will be responsible for providing camera-ready
copy formatted in accordance with instructions to be supplied at the time of
the notification of acceptance. In order for a paper to appear in the
proceedings, at least one of the authors must register for the conference by
the deadline for camera-ready copy (April 29, 1997).
A limited number of student travel scholarships will be offered by ICCBR-97.
Full information on the application procedure will be posted to http://www.iccbr.org/iccbr-97.html
by January 1, 1997. Applications for
student scholarships will be due on February 25, 1997. Students attending
both ICCBR-97 and AAAI-97 may also apply for travel support from the AAAI
student travel grant program.
Program Committee
-----------------
Co-chairs
David Leake Enric Plaza
Computer Science Department IIIA - Artificial Intelligence
Indiana University Research Institute (CSIC)
Lindley Hall 215 Spanish Scientific Research Council
Bloomington, Indiana Campus Universitat Autonoma de Barcelona
47405, U.S.A. 08193 Bellaterra, Catalonia, Spain
Agnar Aamodt University of Trondheim
David Aha Naval Research Laboratory
Klaus Althoff University of Kaiserslautern
Kevin Ashley University of Pittsburgh
Ray Bareiss ILS Northwestern University
Brigitte Bartsch-Spoerl BSR Consulting
Carlos Bento University of Coimbra
Karl Branting University of Wyoming
Ernesto Costa University of Coimbra
Michael Cox Carnegie Mellon University
Boi Faltings EPFL Lausanne
Ashok Goel Georgia Institute of Technology
Kristian Hammond University of Chicago
James Hendler University of Maryland
Thomas Hinrichs ILS Northwestern University
Eric Jones Victoria University of Wellington
Mark Keane Trinity College of Dublin
James King LEXIS-NEXIS
Philip Klahr Inference Corporation
Janet Kolodner Georgia Institute of Technology
Ramon Lopez de Mantaras IIIA-CSIC
Robert Macura Medical College of Georgia
Michel Manago AcknoSoft
William Mark National Semiconductor Corp.
Ashwin Ram Georgia Institute of Technology
Michael Richter University of Kaiserslautern
Christopher Riesbeck ILS Northwestern University
Edwina Rissland University of Massachusetts
Hideo Shimazu NEC
Evangelos Simoudis IBM Almaden Research Center
Derek Sleeman University of Aberdeen
Ian Smith EPFL Lausanne
Gerhard Strube University of Freiburg
Katia Sycara Carnegie Mellon University
Shusaku Tsumoto Tokyo Medical and Dental University
Manuela Veloso Carnegie Mellon University
Angi Voss GMD FIT
Ian Watson Salford University
Stefan Wess Inference GmbH
Sponsors
--------
ICCBR-97 is sponsored by the American Association for Artificial
Intelligence (AAAI), the Catalan Association for Artificial Intelligence
(ACIA), Inference Corporation, LEXIS-NEXIS, and the European Network of
Excellence in Machine Learning (MLnet). Discussions are under way with
additional sponsors.
Additional information
----------------------
All ICCBR-97 information will be available from the ICCBR web site at http://www.iccbr.org.
Please send any questions to iccbr97@iccbr.org.
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>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Mon, 14 Oct 96 15:36:05 CDT
From: dfisher@vuse.vanderbilt.edu
(Douglas H. Fisher)
To: Connectionists@cs.cmu.edu,
bayes-news@stat.cmu.edu,
cogpsy@neuro.psy.soton.ac.uk,
colt@cs.uiuc.edu,
dfisher@vuse.vanderbilt.edu,
dietmar@cognition.iig.uni-freiburg.de,
genetic-programming@cs.stanford.edu,
hybrid-list@cs.ua.edu,
ilpnet@ijs.si,
kdd@gte.com,
ml@ics.uci.edu,
mlnet@swi.psy.uva.nl,
news-announce-conferences@uunet.uu.net,
reinforce@cs.uwa.edu.au,
sigart@vaxa.isi.edu,
uai@maillist.cs.orst.edu
Subject: CFP: 14th International Conference on Machine Learning
Content-Length: 3867
Preliminary Call for Papers
THE FOURTEENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING
July 8-12, 1997
Nashville, Tennessee, USA
The Fourteenth International Conference on Machine Learning
(ICML-97) will be held at Vanderbilt University in Nashville,
Tennessee from July 8 to July 12, 1997. ICML-97 is co-located
with the Tenth Annual Conference on Computational Learning
Theory (COLT-97), and the organizers anticipate fruitful
interactions between the two communities. The ICML-97 program
proper includes a half day of tutorials on July 8 (organized
in conjunction with COLT-97), a technical program that runs
from July 9 through July 11, and a day of workshops on
July 12.
Submissions are invited that describe empirical, theoretical,
and cognitive modeling research in all areas of machine
learning. In addition, papers from related disciplines (e.g.,
information retrieval, statistics, pattern recognition) that
deal with adaptive intelligence, (semi-)automated knowledge
acquisition, or (semi-)automated knowledge organization are
welcome. Submissions that describe the application of machine
learning methods to real-world problems are encouraged, but
such submissions should speak to general issues of machine
learning, perhaps illustrating novel learning methods or
demonstrating the utility of established methods in
previously unexplored settings.
Authors must submit 4 hardcopies of their submissions, as
well as a copy of their title page via electronic mail. The
mailing addresses for hardcopies are:
**Regular Mail** **Express Mail**
Doug Fisher/ICML-97 Doug Fisher/ICML-97
Department of Computer Science Department of Computer Science
Box 1679, Station B 1500 21st Ave. South
Vanderbilt University Room 433, Village at Vanderbilt
Nashville, TN 37235 USA Nashville, TN 37212 USA
615-322-2796 (Express mail forms)
Submissions must arrive by **January 22, 1997.**
Acceptance decisions will be mailed by **March 24, 1997.**
Camera-ready copies will be due by **April 18, 1997.**
A copy of the title page should be sent via electronic mail
to icml97@vuse.vanderbilt.edu
by **January 20, 1997** (note
the date).
The title page should accompany each hardcopy submission, in
addition to being sent through electronic mail. The title
page, both the electronic and hardcopy versions, should be
formatted as follows:
Title:
Author(s) with address(es):
Abstract (200 word maximum):
Keywords:
Email address of contact author:
Phone number of contact author:
Multiple submission statement (if applicable):
The title page should be detachable from the main body of the
paper. The main body should include the paper's title and
abstract, but not the authors, keywords, or contact
information.
The main body (excluding detachable title page, but including
everything else such as title, abstract, figures, tables,
and references) of a submission must not exceed **16** pages
formatted as follows: 12 point font, single-spaced
(Baselineskip = 0.1875 inches or 0.4763 cm), a maximum
per-page text width of 5.5 inches (14.00 cm), and a maximum
per-page text height of 7.5in (19 cm). If the Chair believes
that a submission exceeds 660 square inches or 4258 square
centimeters (under 12pt, single space assumptions), then the
submission will be rejected without review, and an
explanation will be provided to the author(s). Submissions
with text on both sides of each page are encouraged.
For complete submission requirements, as well as information
on registration, housing, workshops, and tutorials, see
KDD Nuggets Index