http:^^www.cs.utexas.edu^users^schwartz^pub.htm

This data set contains WWW-pages collected from computer science departments of various universities

<p>In this paper, we describe P2, a generator of lightweight DBMSs, and explain how it was used to reengineer a hand-coded, 
highly-tuned LWDB used in a production system compiler (LEAPS). We present results that show P2-generated LWDBs 
reduced the development time and code size of LEAPS by a factor of three and that the generated LWDBs executed 
substantially faster than versions built by hand or using an extensible heavy weight DBMS. </p>
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<li><a name="dinesh-thesis">Dinesh Das.</a> <!WA25><a href="ftp://ftp.cs.utexas.edu/pub/predator/das-thesis.ps"><b>Making Database Optimizers More Extensible</b>.</a> Ph.D. Dissertation. Department of Computer Sciences, 
University of Texas at Austin, May 1995. <p>Query optimizers are fundamental components of database management systems (DBMSs). An optimizer consists of three 
features: a search space, a cost model, and a search strategy. The experience of many researchers has shown that hard-wiring 
these features results in an optimizer that is very inflexible and difficult to modify. </p>
<p>Rule-based optimizers have been developed to alleviate some of the problems of monolithic optimizers. Unfortunately, 
contemporary rule-based optimizers do not provide enough support to enable database implementers (DBI) to fully realize the 
potential of open systems. We have identified four requirements that a rule-based optimizer should satisfy to address these 
needs. First, rules should be specified using high-level abstractions, insulating the DBI from underlying implementation details. 
Second, rule sets should be easily extensible, with a minimum of reprogramming required. Third, rule sets should be easily 
reconfigurable, that is, changeable to meet a variety of user needs, interfaces, database schemes, etc. Fourth, rule-based 
optimizers should be fast, that is, performance should not be sacrificed for the sake of high-level specifications. </p>
<p>In this dissertation, we describe Prairie, an environment for specifying rules for rule-based optimizers that satisfies all four of the 
above requirements. The Prairie specification language is presented and we show how it allows a DBI to design an easily 
extensible rule set for a rule-based optimizer. Experimental results are presented using the Texas Instruments Open ODD 
optimizer rule set to validate the claim of good performance using Prairie. Finally, a building blocks approach of constructing 
rule sets is presented; this results in easily reconfigurable rule sets whose features are changeable simply by assembling the 
blocks in various ways. </p>
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<li><!WA26><img src="http://www.cs.utexas.edu/users/schwartz/best.gif" align=right hspace=0 width=88 height=35><a name="adage">Don Batory, Lou Coglianese, Mark Goodwill, and Steve Shaver.</a> <!WA27><a href="ftp://ftp.cs.utexas.edu/pub/predator/adage-arch.ps"><b>Creating Reference Architectures: An 
Example from Avionics</b>.</a> In <cite>Proceedings of the Symposium on Software Reusability</cite>, Seattle Washington, 
April 1995. <p>ADAGE is a project to define and build a domain-specific software architecture (DSSA) environment for assisting the 
development of avionics software. A central concept of DSSA is the use of software system generators to implement 
component-based models of software synthesis in the target domain. In this paper, we present the ADAGE component-based 
model (or reference architecture) for avionics software synthesis. We explain the modeling procedures used, review our initial 
goals, and examine what we were (and were not) able to accomplish. The contributions of our paper are the lessons that we 
learned; they may be beneficial to others in future modeling efforts. </p>
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<li><a name="cia10-95">Don Batory, Lou Coglianese, Steve Shafer, and Will Tracz.</a> <!WA28><a href="ftp://ftp.cs.utexas.edu/pub/predator/cia10-95.ps.Z"><b>The ADAGE Avionics Reference Architecture</b>.</a> In <cite>AIAA 
Computing in Aerospace-10 Conference</cite>, San Antonio, March 1995. <p>ADAGE is a project to define and build a domain-specific software architecture (DSSA) environment for avionics. A central 
concept of ADAGE is the use of generators to implement scalable, component-based models of avionics software. In this 
paper, we review the ADAGE model (or reference architecture) of avionics software and describe techniques for avionics 
software synthesis. </p>
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<li><a name="icde-11">Dinesh Das and Don Batory.</a> <!WA29><a href="ftp://ftp.cs.utexas.edu/pub/predator/icde-11.ps"><b>Prairie: A Rule Specification Framework for Query Optimizers</b>.</a> In <cite>Proceedings 11th 
International Conference on Data Engineering</cite> (Taipei), March 1995. <p>From our experience, current rule-based query optimizers do not provide a very intuitive and well-defined framework to define 
rules and actions. To remedy this situation, we propose an extensible and structured algebraic framework called Prairie for 
specifying rules. Prairie facilitates rule-writing by enabling a user to write rules and actions more quickly, correctly and in an 
easy-to-understand and easy-to-debug manner. </p>
<p>Query optimizers consist of three major parts: a search space, a cost model and a search strategy. The approach we take is 
only to develop the algebra which defines the search space and the cost model and use the Volcano optimizer-generator as our 
search engine. Using Prairie as a front-end, we translate Prairie rules to Volcano to validate our claim that Prairie makes it 
easier to write rules. </p>
<p>We describe our algebra and present experimental results which show that using a high-level framework like Prairie to design 
large-scale optimizers does not sacrifice efficiency. </p>
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<li><a name="ecbs-95">Don Batory, David McAllester, Lou Coglianese, and Will Tracz.</a> <!WA30><a href="ftp://ftp.cs.utexas.edu/pub/predator/ecbs-95.ps.Z"><b>Domain Modeling in Engineering of Computer-Based 
Systems</b>.</a> In <cite>Proceedings of the 1995 International Symposium and Workshop on Systems Engineering of Computer 
Based Systems</cite>, Tucson, Arizona, February 1995. <p>Domain modeling is believed to be a key factor in developing an economical and scalable means for constructing families of 
related software systems. In this paper, we review the current state of domain modeling, and present some of our work on the 
ADAGE project, an integrated environment that relies heavily on domain models for generating real-time avionics applications. 
Specifically, we explain how we detect errors in the design of avionics systems that are expressed in terms of compositions of 
components. We also offer insights on how domain modeling can benefit the engineering of computer-based systems in other 
domains. </p>
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<li><a name="tr-95-06">Lance Tokuda and Don Batory.</a> <!WA31><a href="ftp://ftp.cs.utexas.edu/pub/predator/tr-95-06.ps.Z"><b>Automated Software Evolution via Design Pattern Transformations</b>.</a> In <cite>Proceedings of 
the 3rd International Symposium on Applied Corporate Computing</cite>, Monterrey, Mexico, October 1995. Also TR-95-06, 
Department of Computer Sciences, University of Texas at Austin, February 1995. <p>Software evolution is often driven by the need to extend existing software. &quot;Design patterns&quot; express preferred ways to extend 
object-oriented software and provide desirable target states for software designs. This paper demonstrates that some design 
patterns can be expressed as a series of parameterized program transformations applied to a plausible initial software state. A 
software tool is proposed that uses primitive transformations to allow users to evolve object-oriented applications by visually 
altering design diagrams. </p>
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<li><a name="tr-95-04">Jeff Thomas and Don Batory.</a> <!WA32><a href="ftp://ftp.cs.utexas.edu/pub/predator/tr-95-04.ps.Z"><b>P2: An extensible Lightweight DBMS</b>.</a> Technical Report TR-95-04, Department of 
Computer Sciences, University of Texas at Austin, February 1995. <p>A lightweight database system (LWDB) is a high-performance, application-specific DBMS. It differs from a general-purpose 
(heavyweight) DBMS in that it omits one or more features and specializes the implementation of its features to maximize 
performance. Although heavyweight monolithic and extensible DBMSs might be able to emulate LWDB capabilities, they 
cannot match LWDB performance. </p>
<p>In this paper, we explore LWDB applications, systems, and implementation techniques. We describe P2, an extensible 
lightweight DBMS, and explain how it was used to reengineer a hand-coded, highly-tuned LWDB used in a production system 
compiler (LEAPS). We present results that show P2-generated LWDBs for LEAPS executes substantially faster than versions 
built by hand or that use an extensible heavyweight DBMS. </p>
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<li><!WA33><img src="http://www.cs.utexas.edu/users/schwartz/best.gif" align=right hspace=0 width=88 height=35><a name="tr-95-03">Don Batory and Bart J. Geraci.</a> <!WA34><a href="ftp://ftp.cs.utexas.edu/pub/predator/drc-orlando.ps.Z"><b>Validating Component Compositions in Software System Generators</b>,</a> In 
<cite>Proceedings of the International Conference on Software Reuse '96</cite> (Orlando), 1996. See <!WA35><a href="#ieee-icsr">IEEE TSE 
journal version</a>. Also, Expanded Technical Report <!WA36><a href="ftp://ftp.cs.utexas.edu/pub/predator/tr-95-03.ps.Z">TR-95-03</a>, Department of Computer Sciences, University of Texas at 
Austin, June 1995. <p>Generators synthesize software systems by composing components from reuse libraries. In general, not all syntactically correct 
compositions are semantically correct. In this paper, we present domain-independent algorithms for the GenVoca model of 
software generators to validate component compositions. Our work relies on attribute grammars and offers powerful 
debugging capabilities with explanation-based error reporting. We illustrate our approach by showing how compositions are 
debugged by a GenVoca generator for container data structures. </p>
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<li><a name="sigsoft94">Don Batory, Jeff Thomas, and Marty Sirkin.</a> <!WA37><a href="ftp://ftp.cs.utexas.edu/pub/predator/sigsoft-94.ps"><b>Reengineering a Complex Application Using a Scalable Data Structure 
Compiler</b>.</a> In <cite>Proceedings of the ACM SIGSOFT '94 Conference</cite> (New Orleans), December 1994. <p>P2 is a scalable compiler for collection data structures. High-level abstractions insulate P2 users from data structure 
implementation details. By specifying a target data structure as a composition of components from a reuse library, the P2 
compiler replaces abstract operations with their concrete implementations. </p>
<p>LEAPS is a production system compiler that produces the fastest sequential executables of OPS5 rule sets. LEAPS is a 
hand-written, highly-tuned, performance-driven application that relies on complex data structures. Reengineering LEAPS using 
P2 was an acid test to evaluate P2's scalability, productivity benefits, and generated code performance. </p>
<p>In this paper, we present some of our experimental results and experiences in this reengineering exercise. We show that P2 
scaled to this complex application, substantially increased productivity, and provided unexpected performance gains. </p>
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<li><a name="millie-thesis">Emilia E. Villarreal.</a> <!WA38><a href="ftp://ftp.cs.utexas.edu/pub/predator/villarreal-thesis.ps"><b>Automated Compiler Generation for Extensible Data Languages</b>.</a> Ph.D. Dissertation. Department of 
Computer Sciences, University of Texas at Austin, December 1994. <p>To meet the changing needs of the DBMS community, e.g., to support new database applications such as geographic or 
temporal databases, new data languages are frequently proposed. Most offer extensions to previously defined languages such 
as SQL or Quel. Few are ever implemented. The maturity of the area of data languages demands that researchers go beyond 
the proposal stage to have hands-on experience with their languages, if only to separate the good ideas from the bad. Tools 
and methodologies for building families of similar languages are definitely needed; we solve this problem by automating the 
generation of compilers for data languages. </p>
<p>Our work, Rosetta, is based on two concepts. First, underlying the domain of data languages is a common backplane of 
relational operations. Backplane operations are primitive building blocks for language execution and construction, where a 
building block has standardized semantics. The definition of a well-designed backplane is implementation-independent; that is, 
the backplane is defined once but can be used to model arbitrarily many data languages. </p>
<p>Second, there exist primitive building-blocks for language construction. From our analysis of the database data language 
domain, we have identified three classes of building-blocks: one class maps language syntax to backplane functions, another 
builds an internal representation of the backplane operator tree, and a third class manages contextual information. </p>
<p>For modeling data languages, we define the Rosetta specification language, a grammar-based specification language tailored to 
our needs with the power to define syntax, map it to the target language, and build an operator tree all in one rule. Thus each 
rule is a microcosmic model of a language clause which encapsulates input parsing and code generation. </p>
<p>Our specification language models data languages based on the composition of primitive building blocks for semantics and the 
customization of the syntax for invoking the compositions. A compiler for a data language is generated by first modeling the 
language and then compiling the specification. The ease and efficiency with which Rosetta customizes languages derives from 
the reuse of the backplane operations and the high-level specification supported. </p>