EXHIBIT W8.1
Information Technologies Considered Important
for Counterterrorism
Information Technology
Description
Biometrics Identity and/or verify human terrorist (or watchlist) subjects using 2D and 3D modeling approaches over a variety of biometric signatures: face, gait, iris, fingerprint, voice. Also exploit multiple sensor modalities, EQ, IR, radar, hyper-spectral.
Categorization, Clustering Employ numerous technical approaches (natural language processing, AI, machine learning, pattern recognition, statistical analysis, probabilistic techniques) to automatically extract meaning and key concepts from (un)structured data and categorize via an information model (taxonomy, ontology). Cluster documents with similar content.
Database Processing Ensure platform, syntactic and semantic consistency, and interoperability of multiple types of data stored on multiple storage media (disk, optical, tape) and across multiple database management systems. Desirable aspects include flexible middleware for: data location transparency and uncertainty management, linguistically relevant querying tuned for knowledge discovery and monitoring scalability and mediation, scheme evolution and metadata management, and structuring unstructured data.
Event Detection and Monitor simple and complex events and notify users (or applications) in real time of their Notification detection. Monitoring can be scheduled a priori, or placed on an ad hocbasis driven by user demands. When an event is detected, automatic notifications can range from simple actions (sending an alert, page, or e-mail) to more complex ones (feeding information into an analytics system).
Geospatial Information Fuse, overlay, register, search, analyze, annotate, and visualize high-resolution satellite and Exploitation aerial imagery, elevation data, GPS coordinates, maps, demographics, land masses, and
political boundaries to deliver a streaming 3D map of the entire globe.
Information Management Collect, ingest, index, store, retrieve, extract, integrate, analyze, aggregate, display, and and Filtering distribute semantically enhanced information from a wide variety of sources. Allow for
simultaneous search of any number of information sources, sorting and categorizing various items of information according to query relevance. Provide an overall view of the different topics related to the request, along with the ability to visualize the semantic links relating the various items of information to each other.
Infrastructure Provide comprehensive infrastructure for capturing, managing, and transferring knowledge and business processes that link enterprise software packages, legacy systems, databases, work-flows, and Web services, both within and across enterprises. Important technologies include Web services, service-oriented grid-computing concepts, extensible component-based modules, P2P techniques, and platforms ranging from enterprise servers to wireless PDAs, Java, and Microsoft.net implementations.
Knowledge Management, Use Semantic Web, associative memory, and related technologies to model and make explicit Context Development (expose via Web services) an analyst’s personal preferences, intellectual capital,
multi-dimensional knowledge, and tacit understanding of a problem domain.
Predictive Modeling Predict future terrorist behaviors, events, and attacks, based on past examples and by exploiting a variety of promising approaches, including neural networks, AI, behavioral sciences techniques, subject matter expertise, and red teams.
Publishing Generate concise accurate summaries of recent newsworthy items, ensuring users see topics only once, regardless of how many times the item appears in data or in the press.
Searching Allow users to perform more complete and meaningful searches (free text, semantic, similarity, partial or exact match) across a multitude of geographically dispersed, multilingual, and diverse (un)structured information repositories within and across enterprises (any document type located on file servers, groupware systems, databases, document management systems, Web servers).
Semantic Consistency, Exploit ontologies, taxonomies, and definitions for words, phrases, and acronyms using a variety Resolving Terms of schemes so users have a common and consistent understanding of the meaning of words
in a specific context. Resolve semantic heterogeneity by capitalizing on Semantic Web technologies.
EXHIBIT W8.1
(continued )
Information Technology
Description
Video Processing Analyze, detect, extract, and digitally enhance (reduce noise, improve image color and contrast, and increase resolution in selected areas) user-specified behaviors or activities in video (suspicious terrorist-related activities).
Visualization Provide graphical displays, information landscapes, time-based charts, and built-in drill-down tools to help analysts and investigators discover, discern, and visualize networks of inter-related information (associations between words, concepts, people, places, or events) or visually expose nonobvious patterns, relationships, and anomalies from large data sets. Workflow Management Create optimized workflows and activities-based business process maps using techniques such
as intelligent AI engines by watching, learning, and recording/logging the activities of multiple users using multiple applications in multiple sessions.
Source:Popp, R., et al. “Countering Terrorism Through Information Technology.” Communications of the ACM, March 2004. © 2004 AMC, Inc. Used with permission.
EXHIBIT W8.2
Classification of Dimensions of Virtual Learning
Environments (VLEs)
Dimension
Definition
Comparison
Time The timing of instruction. VLEs free When instruction is delivered asynchronously in a VLE, participants participant from time constraints. retain control as to when they engage in the learning experience.
Learners determine the time and pace of instruction.
Place The physical location of instruction. Participants access the learning material and communicate with VLEs free participants from geographical classmates and instructors through networked resources and a constraints. computer-based interface, rather than face-to-face in a classroom. Space The collection of materials and resources While it is feasible to expand the traditional model of
classroom-available to the learner. VLEs provide based instruction to include the variety of resources available in access to a wide array of resources. VLEs (Leidner and Jarvenpaa 1993, 1995), generally these materials
remain only a secondary resource in instructor-led classroom education. Technology The collection of tools used to deliver In VLEs technology is used to deliver learning material and to
learning material and to facilitate facilitate many-to-many communication among distributed communication among participants. participants. Text, hypertext, graphics, streaming audio and video,
computer animations and simulations, embedded tests, and dynamic content are some examples of delivery technology. Electronic mail, online threaded discussion boards, synchronous chat, and desktop videoconferencing are some examples of communication technology.
Interaction The degree of contact and educational VLEs rely on information and communication technology to create exchange among learners and between the venue of knowledge transfer and learning progress. Unlike learners and instructors. computer microworlds, VLEs are open systems that allow for
communication and interaction among the participants. Unlike traditional classroom education, VLEs support student-to-student and student-to-instructor connectivity throughout the learning experience in a technology-mediated setting.
Control The extent to which the learner can A certain degree of learner control can be built into traditional control the instructional presentation. classroom instruction, but VLEs have the potential to provide far Control is a continuum enabling the greater personalization of instruction and a much higher degree of design of varying degrees of learner learner control than traditional classroom education. Traditional control. learning environments allow students, when outside of the
classroom, to control the pace and sequence of material, and the time and place of their study. VLEs, however, provide this flexibility duringinstruction as well.
Source:Table 1 from G. Piccoli, R. Ahmad, and B. Ives, et al. “Classifications of E-Learning Environments,” MIS Quarterly, (25:4), 2001, pp. 401–426. Copyright © 2001 by the Regents of the University of Minnesota. Reprinted with permission.
EXHIBIT W8.3
KM-Enabling Technologies
Knowledge Principles
Knowledge Methods and Tools
Knowledge Is Not Merely Data
• Descriptive data are not enough for pur-poses of decision making.
• Analysis is required to turn data into patterns (insights) and understanding. Knowledge Needs to Change as the World Changes
• Knowledge, as stock, rarely remains stag-nant: beliefs and assumptions change over time.
• We need to keep what we know in sync with change in the world around us. Knowledge Processes Require Reasoning • Transforming data into patterns (insight)
requires inferences and judgments—in short, thinking.
• There is a need to organize and aid how individuals and groups engage in think-ing, etc.
Knowledge Is Often Implicit or Tacit • We know more about customers,
technol-ogy, etc., than we can articulate. • A lot of “know-how” remains tacit but is
critical to what we do and how we do it. Knowledge Cannot Be Separated from the “Knowers”
• We cannot separate “what we know” from the individuals who know it. • It is largely impossible to separate what
we know from what we do in our day-to-day work and lives.
• Knowing and doing are intimately inter-connected (to the point that it is terri-bly difficult to disentangle how they influence each other).
Knowledge Is Difficult and Often Impossible to Manage Directly
• We can only manage knowledge through influencing the “knower.”
• We can manage knowledge indirectly by managing the organization: its culture, people, technologies, structures, and systems, strategies, etc.
• By managing these factors, we can indi-rectly manage knowledge stock (what individuals and groups know) and knowl-edge flow (how knowlknowl-edge moves be-tween and among individuals and groups).
Mentoring:
Communicates the organization’s values, norms, and practices; exposes tacit understanding of how the world works.
Training and Development:
Convey explicit knowledge in many different types of settings; expose shared tacit viewpoints.
Comprise groups of individuals, often from multiple disciplines or silos, who come together to share what they know, to learn together.
A Knowledge Project:
Brings a group of individuals together with a declared and visible focus and intent to generate a stock of required knowledge.
A Knowledge Repository:
Provides a central location for various knowledge products such as best prac-tices, or analysis of different topics; individuals and groups develop products for the repository, and they in turn provide inputs for further discussion and reflection on the part of others.
Communities of Practice:
Make up a group of individuals who share the same values and intent, work on a collective project or endeavor, and share openly and critically with each other.
Intermediary Roles:
Are held by one or more individuals who take responsibility for developing a specific stock of knowledge, a plan to share it with others, etc.
Storytelling:
Is done by developing a story about “how some things happen around here” or “what we did in this project” as a way to communicate a sense of purpose, to espouse shared values, and to get at more implicit forms of knowledge. Collaboration:
Formally gets a set of individuals to come together around a specific task or project so that they can learn from each other.
Social Network Analysis:
Identifies and communicates who speaks to whom, and how information is trans-mitted from one individual to another, or from one group or department to another.
Scenarios:
Brings individuals both from inside and outside the organization to develop explicit stocks of knowledge about the future (such as how an industry might evolve or how a set of technologies might converge over time).
Knowledge Mapping:
Identifies who knows what, how stocks of knowledge are related to each other, how the information is stored and where, etc.
Experiments:
Allow one or more individuals to do something on a small scale that otherwise would not be done as a means to learn about (for example) how electronic connections might work, what data they might generate, or how customers or others might engage with different forms of electronic connections.
EXHIBIT W8.3
(continued )
Knowledge Principles
Knowledge Methods
Knowledge Types:
• Can be explicit, meaning objective, docu-mented and clear. Or it can be tacit, which is subjective, stored insights and experiences.
Knowledge Server:
Contains KM software, including knowledge repository, and provides access to knowledge.
Enterprise Knowledge Portal:
A single access point into a KM system; organizes unstructured information and knowledge.
Knowledge-Harvesting Tools:
Capture organizational knowledge unobtrusively. Electronic Document Management:
Allows user to access documents over the Internet; allows electronic collaboration on document creation, revision, or sharing.
Source:Fahey, L., et al. “Linking E-Business and Operating Processes: The Role of KM.” IBM Systems Journals40, no. 4 (2001).
Source:Kwok, S. H., et al. “Peer-to-Peer Technology Business and Service Models: Risks and Opportunities.” Electronic Markets12, no. 3 (2002). Courtesy of Taylor & Francis Ltd., tandf.co.uk.journals.
P2P Service
Model
Layer 3: Business Model Create and maximize
revenues
Layer 2: Community Share as much useful
content as possible Layer 1: Technology Allow users to communicate and exchange content Performance Measurement Customer Base Revenues Content Selection (variety and quality)
Transaction Volume Implementation of P2P Function Degree of User Friendliness Success Factors Customer Acquisition Revenue Generation Value Creation Value Distribution Technical Feasibility Usability
