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Active Catalog: A Knowledge-Rich Design Library Facilitating Information Consumption Ping Luo, Peter Will, and Houchang Lu University of Southern California Information Sciences Institute 4676 Admiralty Way, Marina del Rey, CA 90292 E-mail: {ping, will, hclu}@isi.edu
ABSTRACT Today’s engineering design task often consists of combining a large number of reusable components from design libraries taken from related domains. Although, perhaps, rich in content, today’s libraries are poor in using domain knowledge to help designers access library contents during the course of design. In this paper, we describe a prototype system, ACTIVE CATALOG, that utilizes a rich body of domain knowledge to facilitate the access of library contents. The domain knowledge-base also provides a vocabulary that is at a much higher level of conceptualization than those used by traditional database query or the key-word match technique widely employed by Web applications. Based on the knowledge of the domain and the meaning of the queries specified in this vocabulary, a search engine accesses library components that satisfy the “semantics” defined by the queries. This is a much deeper match than the usual syntactical key-word match. ACTIVE CATALOG’s facility on accessing library contents goes even further. ACTIVE CATALOG contains, suggested by its name, active information together with a rich set of models of different modalities that can be used to extend traditional library contents and that enable a designer to down-load models, viewers or even simulation code to evaluate dynamic and behavioral aspects of a component via interactive simulation. KEYWORDS:
Knowledge-based Information Retrieval, Semantic Network, Database-Knowledgebase Integration, Design Library, Simulation-based Design. INTRODUCTION
Today’s design environments and tools, in the domain of hardware and software, electronic and mechanical, to facilitate designers’ work, must provide them with, in addition to the traditional tool sets, libraries and catalogs of reusable components in relevant design domains [1]. AUTODESK’s Parts Library [2] for mechanical design and SmartModel Library [3] for circuit design are examples. The major benefits of using these libraries and catalogs are obvious: design by reusing components, results in products that are cheaper, better and faster to market. With the ability to save partial designs, designers can cache existing (design and engineering) results for future applications and thus, bootstrap their effort.
However, most of the above benefits of catalogs and libraries are based on one major assumption: that the information is easily and conveniently accessible (i.e., reachable and usable) to the designer. In general, this assumption does not naturally hold. Although rich in content, today’s libraries are poor in access. The situation would be improved by using domain knowledge to give designers easier access to library contents during the course of design. There exists a gap between the high-level, conceptual mental model of what a designer needs and the low-level, physical query that retrieves the needed information from a library. Not surprisingly, a human designer who uses the libraries and catalogs is forced to make a special effort to fill this gap. Too often, the designer is required mentally to map a desirable component for solving a particular problem into a set of searchable attributes recognizable by the library/catalog. That is, to access the contents, the user of the libraries needs to understand the “schema” of the libraries and the searchable technical attributes of the library contents, in addition to the thorough understanding of many other issues of the domain and design. In a heavily constrained design situation, such as electro-mechanical design, this mapping will not be trivial and imposes a major mental burden to designers, deflecting them from focusing on directly solving the design problem. In this paper, we describe a prototype system, ACTIVE CATALOG, that addresses the above problems by using a rich body of domain knowledge to describe the library components, adding to and annotating traditional library information. With ACTIVE CATALOG, designers can search library contents using the terminology of the domain familiar to them. A search engine, based on the knowledge of the domain and the meaning of the queries specified in the vocabulary, can access library components that satisfy the functionality meant by the queries, however, the search will not necessarily match the keywords or synonyms in the queries. ACTIVE CATALOG’s facility for information access goes even further. ACTIVE CATALOG contains, suggested by its name, active information together with a rich set of models of different modalities that extend traditional library functions. These, in turn, enable a designer to evaluate dynamic and behavioral aspects of a component via interactive simulation. The goal of this research is to improve the consumption of
information in catalogs and libraries, but done here in the context of engineering design. We approach this goal by addressing potential solutions to several fundamental HCI, cognitive issues: we, humans, are much less productive when we are diverted by too many distracting tasks; we cannot keep track of many threads or recall all what we know. We cannot perform high quality reasoning and provide correct judgement if there exists no direct mapping between a real world object and our mental model of that object; and last, but not least, we cannot access material if we are not aware of its existence. ACTIVE CATALOG is aimed at providing technologies that enable designers to dedicate their mental resources to the design problems at hand. ACTIVE CATALOG uses a knowledge-based approach to helping the user. The rest of the paper is organized as follows. We first give an overview of the system architecture by illustrating system substrates. We then describe in detail the key system components that are relevant to knowledge-based facilities. These components include a semantic network of the domain that enables a high-level, flexible, and user-centered approach to information query; a knowledge-based search mediator that hides distributed and heterogenous information sources from a user; and a set of middle-ware that enables a distributed simulation environment to assist in evaluating the dynamic and behavioral aspects of a piece of information retrieved. One section will be dedicated to each of these components, followed by reviewing of related work and our future directions. OVERVIEW OF THE SYSTEM STRUCTURE
An ACTIVE CATALOG, as the name suggests, is a catalog containing active, i.e., behavioral and dynamic, information of design components and parts described by a rich set of models of different modalities to facilitate information consumption. The modalities of models include, for example, a mathematical model, simulation model, engineering drawing, 3D geometrical model, electronic model, textural and semantic description, a set of information viewers and even simulation code. The major components or substrates of an ACTIVE CATALOG (FIGURE 1) are a semantic model of a domain, a set of models in the above mentioned modalities, a set of databases that stores the models, a search engine that recognizes users’ queries specified in a vocabulary given by the semantic model of the domain, and a set of helper applications for viewing the corresponding types of models. An ACTIVE CATALOG will also provide a set of application programming interfaces (API) to facilitate the integration of a catalog into engineering and electronic commerce environments. A user of ACTIVE CATALOG, as shown in FIGURE 1, can either browse catalog contents or issue high-level conceptual queries for the desirable information. A knowledge-based search engine, taking as input the user’s high-level query and the domain knowledgebase, matches catalog contents satisfying the semantics of the query. When a piece of information is returned by the search engine, the
system will factor the information into different modalities and dispatch these pieces to corresponding helper applications/viewers handling the modalities. This aids the user to evaluate the information in different dimensions. When necessary, a distributed simulation environment will be automatically configured to provide such needs. Rather than a lengthy description of every dimensions of an ACTIVE CATALOG, in the following sections, we will focus on its capability of providing knowledge-based support to engineers’ work. SEMANTIC NETWORK DESCRIBING THE DOMAIN
A semantic network [4] is the key to knowledge-facilitated search for information. The semantic network in ACTIVE CATALOG captures explicitly the true nature of its domain. It describes taxonomies of the domain objects (parts and components in the catalog or library); their attributes, values and value constraints; taxonomies of the attributes, values and value constraints; and relationships among these objects, attributes, and attribute values and value constraints. The semantic network is modeled in a knowledge representation system called Loom [5] developed at ISI. Loom is a language and environment for constructing intelligent applications. The heart of Loom is a knowledge representation system that is used to provide deductive support for the declarative portion of the Loom language. Declarative knowledge in Loom consists of definitions, rules, facts, and default rules. The semantic model of the current domain in ACTIVE CATALOG consists mainly of definitions. ACTIVE CATALOG’s existing semantic network describes FIGURE 1. ACTIVE CATALOG Architecture: supporting information consumption from retrieval to application.
pump systems. This is due to the driving application domain being electro-mechanical design in the context of ship design and maintenance. Although the domain is specific to pump systems, the technologies and mechanisms developed are domain independent and, thus, are not limited in any way. Main Taxonomy for Pumps
The main taxonomy of the semantic network describes pump types. It is similar to typical product classifications found in traditional catalogs, however, the ACTIVE CATALOG’s classifications are much more detailed. The pump taxonomy is organized as a graph rather than a tree structure by taking advantage of LOOM’s multiple inheritance. This provides multiple paths to a specific pump type. At the top levels of the taxonomy are a few abstract type-definitions for pumps, such as Kinetic Pumps and Positive Displacement Pumps, with Special Pumps, Peripheral Pumps and Centrifugal Pumps being subclasses of Kinetic Pumps, and Rotary Pumps, Blow Case Pumps, and Reciprocating Pumps being subclasses of Positive Displacement Pumps. In the middle levels are a rich set of abstract classes defining varieties of pump classes. At the bottom levels a large number of pump classes are defined by combining types from top and middle levels. For example, Reciprocating Single Acting Power Multiplex Pumps are defined as sub-class of Reciprocating Pumps, Single Acting Power Pumps, and Multiplex Pumps. FIGURE 2 is a sample pump taxonomy in our semantic net. The structure of this taxonomy has several benefits. Since most bottom-level, specific pump types have multiple parents, the users of the taxonomy can reach the same target types via different paths. This enables the user to issue partially specified queries in terms of known pump types/ features without sacrificing the capability of reaching target information. The taxonomy used in ACTIVE CATALOG offers greater flexibility over the traditional one and does not require its user to know and remember the exact types of the pumps needed. For example, a pump of type Reciprocating, Single Acting, Power Multiplex Pump is reachable via any of (or combination of) the following pump types: Reciprocating Pump, Single Acting Power Pump, Multiplex Pump, and all the pump types that are the parents (in the type hierarchy) of these three types.
for an attribute either in the context of a pump type or in a context-free form. All value constraints constitute the space of attribute values. As in types, the value constraints are organized hierarchically into a taxonomy. For example, the value constraint for the attribute, Casting Material of Pumps, is Solid Material, which could be Casting Iron, High Silicon Iron, or Naval Bronze. By combining the above taxonomies, one can construct a specific query such as, Pumps whose Casting Structure is Bronze, or a more conceptual query, Pumps whose Structure is Corrosive-resistant Material (Bronze is also one of its sub-classes). Of course the first query is more specific and precise, however, it requires more knowledge about pumps and more skill to specify; whereas the second one will more likely return a larger set in the result, but will be a lot easier to construct. Although both forms are needed, the second one is preferred in many situations since it requires less mental effort. In general, it is much harder to generate a correct set of results by one precise query than to achieve the same set by filtering from a larger set of candidates (if the size of the set of candidate is manageable). FIGURE 2. Sample taxonomies of pump types, attributes and value restrictions from ACTIVE CATALOG’s semantic network of the domain. Type Hierarchy
Value Constraint Hierarchy
Main Taxonomy for Attributes and Value Constraints
Attributes describe properties of the objects in the semantic network. A unique feature of attributes is that, like pump types, they are organized hierarchically in ACTIVE CATALOG’s semantic network in order to facilitate query specification and reasoning. For example, attribute Pump Structure is a high-level abstract attribute, with several subattributes: Propeller Structure, Prime Mode, Internal Structure, and so on. With an attribute taxonomy, one can ask loosely for the Structure of a pump being of SelfPriming or Peripheral rather than specifically Priming Structure being Self-Priming and Internal Structure being Peripheral respectively. A value constraint specifies the range of all possible values
Attribute Hierarchy
Centrifugal Pump Axial Flow Centrifugal Pump Mixed Flow Centrifugal Pump Radical Flow Centrifugal Pump Screw Centrifugal Pump Special Pump Electromagnetic Pump Gas Lift Pump Hydraulic Ram Pump Jet Pump Pitot Pump Screw Centrifugal Pump Viscous Drag Pump Construction Casing Construction Coating And Lining Construction Impeller Construction Wearing Ring Construction Driver Brake Horsepower Driver Position Driver Type Pitch Installation Geometry Mounting Weight Impeller Structure Impeller Structure By Flow Direction Impeller Structure By Openess Rotor Structure Bearing Structure Circumferential Piston Structure Diaphragm Structure Flexible Member Structure Gear Structure Lobe Structure Piston Structure Screw Structure Vane Structure
Although seemingly more flexible and more user-friendly than traditional information retrieval, the queries so far are still described in technical attributes. That is, the users are required to transform domain problems into technical attributes and terminology and to use technical-oriented vocabulary to construct queries. Users need to be able to ask for what they need in terms of domain problems and applications. Semantic Network
Semantic network, woven with the above (class, attribute, and value constraint) taxonomies, declaratively describes a rich set of domain knowledge that is traditionally used by experienced engineers. A declarative representation of such knowledge is easily sharable, transferrable, and maintainable; it can be used by computer program to automate sophisticated, routine tasks that otherwise have to be performed by human designers. One of such tasks is mapping a semantic representation of an engineering part or component that satisfies the need of a domain problem into a set of representations that can be recognized by information sources storing corresponding parts and components. Our current semantic network in ACTIVE CATALOG captures a sub-set of total knowledge on pump systems, i.e., pumps and motors, their connections, and their applications. FIGURE 3 graphically depicts a piece of the network describing the relationship between pumps and motors and the relationship between casting materials for pumps and the type of fluid pumps can transfer. FIGURE 3. A fraction of pump knowledge sampling semantic network in ACTIVE CATALOG. Connector Connects
Connects Drives Motor
Pump Driven By is a
Transfers Fluid Casting Material
Implies
is a
is a Corrosive Resistant Material
Corrosive Fluid
Second, this model captures knowledge about applications. For example, the network contains a piece of deductive information that if a pump is used to transfer corrosive fluid, then the pump’s material should be corrosive-resistant. In fact, our network provide a set of different types of corrosive fluid, corrosive resistant materials, and a set of correspond implication rules. So that users can directly state their applications, such as a high capacity pump for transferring sea water or a motor that drives a high-capacity centrifugal pump, without having to know the exact technical terms for describing pump parameters. Third, the network facilitates information accessing even if the information is unknown to the user. When a new type of material becomes available we make the new material retrievable by simply adding in the model for the new material and implication rules without touching other parts of the network. All the changes are transparent to the end users. That is, even an old query, for example, the query above, will automatically take into account the new type of material although the user is not aware it existence and its availability. Fourth, the semantic network captures the true nature of the domain and thus is sharable across domains. The overlapping between fluid and power source for motor reflects this character. Water and air are such instances that fall into the overlapping area. Also since the model captures the true nature in a generic fashion, any model that has this characteristic is conceptually adaptable into the network here. Similar argument holds for the adoption of our model into other knowledge networks. USER-FOCUSED QUERY
is a
High Centrifugal Speed Pump Motor Driven By Powered By
Casting
To avoid a lengthy discussion, we will not describe here every detail about the model since it is fairly selfexplanatory. We here will focus on several interesting points depicted by the model. First, the model has an open structure. It provides a template for expanding into a much richer and detailed network depending on the need of the application. For example, a rich model of fluid, power source, corrosive fluid, casting material, corrosive resistant material, or connector can be added to enrich the depicted model.
Power Source
The semantic model provides a rich vocabulary for use in constructing queries. Therefore, users are able to arrive at same set of information via many channels: by describing attributes and values in technical terms (the traditional way of searching for information), by describing the applications at hands in users domain terminology, or by describing problems they need to solve. For example, the following queries could be semantically equivalent in a given data source of some domain: a centrifugal pump with bronze casting and propeller, a pump that pumps seawater, or even “we need to pump out seawater” (taking into account the context is in ship design). Similarly, users can ask for motors by giving pumps as constraints. User-focused query is the key to accessibility of today’s ever-rich and ever-evolving information. The richness of information offers great potential to leverage users’ work.
However, in today’s dynamic information age too many things (such as the physical locations of information sources, contents of the sources, information formats from the sources and structures of the sources) are changing too often and the user of the information is unnecessarily and undesirably exposed to these changes. (Think of how often we need to upgrade and learn to use new software products in our work for the sake of some new features.) ACTIVE CATALOG’s user-focused query provides a solution to overcome this problem. In ACTIVE CATALOG environment, the user is insulated from these unnecessary changes by a body of stable domain knowledge. A user’s query will be valid over a much much longer period of time even though the physical parameters of information storages may have been totally changed and the information content is totally different and totally new. SEARCH ENGINE
As mentioned previously, the semantic network is declarative. The declarative nature is necessary for being able to share the domain knowledge between human users and the computer. The semantic model provides a vocabulary that is close (if not the same) to the one designers use in their daily work and captures the true nature of and the experts’ knowledge about the domain. So that it is easily understandable by designers who are domain experts. Also, the attributes, their values and value constraints, and all the comprehensive relationships between the objects modeled are explicitly described by the model so that they can be reasoned and understood programmatically. Furthermore, the knowledge can be used to automate the translation of a semantic query into corresponding queries of physical data repositories containing relevant information. The knowledge-based search engine we use, called SIMS [6], is being developed at ISI. SIMS is an intermediate layer - a mediator - between information sources and humans users or applications programs. Queries to SIMS are in a uniform language, independent of the distribution of information over sources, of the various query languages, the location of sources, etc. SIMS determines which data sources to use, how to obtain the desired information, how and where to temporarily store and manipulate data, and how to maintain an acceptable level of efficiency in performing its task. To query physical information sources giving a semantic query specified with the terminology from the semantic network, SIMS needs to convert the semantic query into a set of semantically equivalent queries to the physical information sources. To do so, SIMS needs to know the structure of the information sources and the mapping from semantic terminology into physical data sources, in addition to the domain knowledge given by the semantic net. Of these, the structure information of the data sources can be modeled easily (in fact, majority part of the model can be generated automatically by querying the schema information of the data sources); the KB-DB mapping need to be hand craft, however, it is straightforward engineering work.
With the three pieces of model, the domain model, the information source schema model, and the KB-DB mapping model, SIMS performs reasoning on a semantic query (such as changing “seawater pump” into “bronze casting material”), partitions a potential complex semantic query into many small pieces and transforms the small pieces into actual queries to the physical information sources, taking into account of heterogenous and distributed nature of the sources, efficiency for database joining, parallel accessing information sources, and redundant information storage such as mirroring and partial overlapping. FACILITATOR TO SIMULATION-BASED EVALUATION
Support to engineering design in ACTIVE CATALOG goes beyond the retrieval of information. Major effort in ACTIVE CATALOG has been focused on facilitating its users to evaluate information from information sources, once returned by users’ semantic queries, in an effortless and seamless mode. When a piece of information is retrieved, based on its type, the system dispatches the information to corresponding viewers. For example, if the information is a engineering drawing in AutoCAD [11], the system will invoke AutoCAD environment and load the drawing into AutoCAD; if the information is a differential equation described by a MatLab [7] model, MatLab/ SimuLink will be brought up with the model inserted. In a more sophisticated situation, the system will perform a sequence of tasks, which otherwise have to be handled manually by the user in a traditional environment, to help its user to evaluate the information. For example, when a piece of information is multi-modal, such as in simulated behavioral model, the system will separate the model into different modalities and then dispatch each piece of the model in a specific modality to its corresponding tools. If necessary, even a distributed simulation environment will be configured automatically on the fly to serve such purposes. To illustrate how the system works, let us go through a simple example in our pump domain. Assuming that the piece of information retrieved from our data sources is a simulation model for a pump systems consisting a pump and a motor, where the simulation model has two parts: an animation and a mathematical model. The animation illustrates the behavior of the pump system graphically requiring WorkingModel [8] software to run, whereas the mathematical model describes the dynamics of the pump system in MatLab model that is executable by MatLab/ SimuLink. And, furthermore, the animation is driven by the mathematical model that can only be executed on a different machine (i.e., the user’s machine does not have MatLab/ SimuLink installed). In this scenario, ACTIVE CATALOG will separate the piece of information into two modalities: animation and mathematical model. The animation model will be loaded into WorkingModel software running at the user’s host machine. Executing the mathematical model by its corresponding environment is more complicated and requires the system to automate a sequence of tasks intelligently. Although complicated, the basic idea can be
conceptually depicted briefly as follows. In our example, the system will first send out a message to a Broker (running on a different machine) requesting for MatLab service. The Broker will search its database for hosts that have been registered to the Broker for being capable of providing such service. The search result will be returned to the requester, the ACTIVE CATALOG environment, a host candidate that meets the specification. ACTIVE CATALOG uses this information to contact the host for service. Upon establishing a connection to the host, ACTIVE CATALOG sends out the mathematical model to the server and sets up proper communication channel for data exchange between MatLab and WorkingModel and execute the simulation. Thus, the behavioral model is executed in a distributed environment: animation on one machine driving by a mathematical model executed on another host configured at runtime and hidden from the user. The facilitator for simulation-based design implements “Try Before You Buy”, a powerful, new paradigm of information access. This paradigm, although illustrated here in the domain of pump systems, is not limited to this particular domain or solely to engineering design. Instead it is applicable to a wide range of applications such as education and electronic commerce. Again, as the knowledge-based information query, the facilitator continuously implements our central goal – facilitating information consumption. RELATED WORK
Engineers usually work with catalogs, acquired over the years, that contain descriptions of parts and sub-systems. The catalogs form not only the repository of information describing the particular part but also form pointers to sources of the part. (An obsolete catalog may still be useful in that it points to a source, engineers keep them for this reason). Current, up to date catalogs contain descriptions of parts at a detail sufficient not only for purchase but also for use in a larger system. The catalog often contains a picture of the part, a set of descriptors (size, weight, speed and other appropriate specifications, drawings etc. Often included in the catalog is context information ... “if you use this part you need to buy this widget to make it work”. Sometimes the catalog contains a short technical paper or applications note that tells how the part has been used in a conventional or sometimes unconventional manner. Searching for a part is usually manual to find the catalog and manual via the catalog index to find the part. The process is slightly haphazard. Recently a change is taking place. Catalogs are becoming on-line. For example, IndustryNet [9], PARTNET [10] et al. These can be federated databases or centralized depending on the structuring of the provider’s product offering. Search of these catalogs is mostly by keyword match, with searchable attributes and their values in technical terms. Next, AUOTDESK has produced a CD-ROM of drawings of parts designed in their AUTOCAD product and that can be down loaded into a users system for reuse in the drawing of the design being produced. Logic Modeling Group, Synopsys, Inc. provides libraries of simulation models for
VLSI parts. These models work with the major CAD systems and give the possibility of using the models in design activity. The provision and use of models that can be inserted into CAD systems powerfully improves the user’s ability to produce good designs at a faster rate than normal. The design can be proven before any component is purchased and often eliminates the need for building several sequential physical models. No such design use has been made for mechanical parts. No such design use has been made for parts from any other domain. The ACTIVE CATALOG concept was invented to rectify this omission. FUTURE WORK
Although developing a semantic network that captures the true nature of all objects in the universe of engineering design is impossible, a subset of focused domain still leverages designers ability of accessing information significantly. Even though, constructing a close-to-complete semantic model of a small domain requires a great amount efforts in knowledge acquisition and engineering. Since ACTIVE CATALOG has already demonstrated the need of such a semantic model, we next will test the extensibility of our framework by utilizing existing domain knowledgebases and ontology work from other institutes and industry. Therefore, we will focus on middle-ware technology to coordinate and reconcile knowledgebases. Rather than a monolithic whole, we will build a modularized and easily Customizable system. We plan to use ACTIVE CATALOG’s knowledge-based search component as the front end for a selected commercial catalog/library. Also the use of a set of commercial catalogs/ libraries as the the data repository for ACTIVE CATALOG is also under consideration. Our current implementation of facilitator to simulationbased evaluation captures a body of knowledge about simulation, models, tools, and data exchange. However, the knowledge is implicitly represented by fragment of programming code. We need to convert the knowledge into an explicit format as we understand the simulation issue in more depth. The need for an explicit representation of knowledge about servers and their capabilities has already surfaced during server selection and negotiation for service. We are currently actively working on developing more sophisticated service request, candidate filtering, contract negotiation protocols and algorithms. We are also exploring new modalities to offer more desirable views to information. CONCLUSION
ACTIVE CATALOG, as described here and in its evolution in the future, brings a conceptually new idea to design in the specific and to electronic commerce in general. It gives an electronic version of "Try Before You Buy" where you "try" in whatever modality is correct for "your" domain. The ACTIVE CATALOG supports a wide variety of search mechanisms including functional search and supports the
use of a wide variety of models and modelling systems. The consumption of catalog items in a design or modelling environment is predicated in an adequate set of information interchange mechanisms. ACTIVE CATALOG facilitates engineer’s work by using intensive knowledge of the domain for information retrieval and of the tools and environments for information evaluation. It contributes to knowledge intensive CAD and benefits information accessing (from retrieval to consumption) in general in a variety of dimensions: • Open-ended and modularized domain knowledge model eases knowledge sharing. The knowledge sharing is bidirectional. As discussed previously, the semantic network in ACTIVE CATALOG lets us expend in coverage and enrich in detail our current knowledgebase by plugging in other knowledgebases. Also, because our semantic network captures the true nature (or our best knowledge) of the domain in a generic way and it is serves as a stand-along knowledge module, it can be easily adapted into other knowledgebases and semantic networks. • High-level, flexible, conceptual queries provide userfocused information retrieval. Our knowledge-enabled query allows its users to specify their needs with a vocabulary familiar to their domain. The query can be partially specified without compromising the capability of retrieving the target information; the query can be specified by describing application and the problem of the domain without having to know the technical terminology in the information sources. The capability of supporting user-focused query insulates the user from many undesirable details such as storage format changes, specific terminology used by different pieces of information, and mapping between different terminologies and mental models. • Highly automated task environment facilitates information evaluation. ACTIVE CATALOG currently implicitly captures a body of knowledge about tools for information viewing and evaluation. This knowledge goes way beyond modality of information and correspond helper applications, which is what current Web Browsers posses. it contains procedural knowledge of setting and configuring distributed simulation environment needed for information viewing and evaluation. With this knowledge, ACTIVE CATALOG can even provide a distributed simulation environment at runtime when such a need arise. • Integrated retrieval and evaluation environment offers a seamless information flow. Enabled by a rich body of knowledge of the domain and the tasks for information evaluation, ACTIVE CATALOG offers a high-level task automation. It integrates information retrieval and evaluation as a seamless whole, meanwhile off-loads distracting, resource consuming tasks from designers. We believe that an information-rich environment is vital to both today’s and future’s engineering design. However, its true potential is still yet to be realized since an information-
rich environment may not necessarily, automatically facilitate its users’ work; in fact and ironically, many such systems distract their users from their main focuses. The key effort of ACTIVE CATALOG is to provide a set of enabling technologies that let information users focus on their domains in a seamless fashion throughout their design tasks. ACKNOWLEDGMENT
We wish to thank Shivanand Bhajekar for producing the mathematical models. This work was supported by DARPA MADE Program under contracts J-FBI-95-159. Contents represent the opinions of the authors, and do not reflect official positions of DARPA or any other government agency. REFERENCE
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