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....specified with the available modeling techniques. It is not our goal to create a complete model for the target system or to produce a full performance analysis for it. The target system of our case study is a reduced version of the TPC Web Commerce Benchmark revision D 5. 0 (TPC W) as described in [TPC99]. We have slightly changed the original design that assumes HTTP communication between client and server nodes. In our approach, remote communication is implemented with CORBA, but the functionality of the system and all end user interactions are kept as close as possible to the original design. ....

....implementation does not execute any application code, we executed our tests with the mean of 2 seconds to impose a higher workload for the server node. For normal TPC W benchmarks, this value is used in the overload run for verifying the implementation s capability to withstand excessive workloads [TPC99]. Since each end user interaction contains only a few CORBA invocations, the behavior of the electronic commerce system can be modeled with one workload diagram. The first nine interactions are illustrated in Figure 44 and the last five are shown in Figure 45. The execution probability for each ....

Transaction Processing Performance Council (TPC), TPC Benchmark W (Web Commerce), Revision D-5.0, TPC, San Jose, CA, USA, 1999.

....[7] We construct and evaluate three classifiers. One classifier is built using DSS and OLTP and training data from the TPC H TM [14] and TPCC TM [13] benchmarks, respectively. The second classifier is built using DSS and OLTP training data from the Browsing and Ordering profiles of the TPC W TM [15] benchmark, respectively. The third classifier is a step towards genericness; it is built using training data from all these various workload types. The rest of this paper is organized as follows. Section 2 reviews related work. Section 3 farst describes our approach to the problem and the ....

....the substantial amount of full table and index scan operations. OLTP 4 1.4 1.2 1 0.8 0.6 0.4 0.2 Figure 2: Candidate attributes for snapshot objects applications typically access relatively few random pages. We consider the Browsing and Ordering profiles deftned in the TPC W benchmark [15] as examples of DSS and OLTP workloads, respectively. Figure 2 shows the relative values, with the DSS values normalized to 1, for a set of candidate attributes. The values are derived from experiments with the TPC W workloads on DB2 Universal Database. The candidate attributes are all easily ....

TPC Benchmark W (Web Commerce) Standard Specification Revision 1.7, Transaction Processing Performance Council, October 2001.

....workloads generated by Transaction Processing Performance Council (TPC) benchmarksk These workloads are rtm on a DB2 Universal Database TM Version 7.2 system [7] We construct and evaluate three classifiers. One classifier is built using DSS and OLTP and training data from the TPC H TM [14] and TPCC TM [13] benchmarks, respectively. The second classifier is built using DSS and OLTP training data from the Browsing and Ordering profiles of the TPC W TM [15] benchmark, respectively. The third classifier is a step towards genericness; it is built using training data from all these ....

TPC Benchmark H Standard Specification Revision 1.3.0, Transaction Processing Performance Council, 1999.

.... have implemented our Active Dependency Discovery (ADD) technique and have applied the implementation to characterize a subset of the dependencies in a prototype e commerce environment based upon the TPC W web commerce benchmark, which simulates the behavior of an online bookseller s web storefront [11]. In particular, we used the ADD approach to generate a dependency graph for each of 14 distinct end user interactions supported by the TPC W environment; each such graph maps the dependencies between one user interaction and the particular database tables upon which that interaction depends. The ....

TPC Benchmark W Specification vl.0.1, Transaction Processing Performance Council, San Jose, CA, 2000, http://www. tpc.org/wspec.html.

....automatically and dynamically adjusts aspects of its workload according to the performance characteristics of the system being measured. By doing so, the benchmark automatically scales across a wide range of current and future systems.This scaling is more general than the scaling found in TPC B [TPC90] and LADDIS [Wittie93] for scaling here varies more than the load on the system. This first step aids in understanding system performance by reporting how performance varies according to each of five workload parameters; these parameters determine the first order performance effects in I O ....

TPC Benchmark B Standard Specification. Technical report, Transaction Processing Performance Council, August 1990.

....the read modify write operation can be improved by acquiring the old contents of the data unit to be updated from the system s buffer cache rather than reading it from disk. This reduces the number of disk operations required from four to three. This situation is very common in OLTP environments [TPCA89, Menon92c]. When the number of data units being updated exceeds half of one parity stripe, there is a more efficient mechanism for updating the parity. In this case, the controller writes the new data without pre reading the old contents of the written unit, reads and XORs together all of the data units in ....

The TPC-A Benchmark: A Standard Specification, Transaction Processing Performance Council, 1989.

....XML data generator for our community. 1 Introduction Synthetically generated data is very useful in evaluating and understanding new ideas in database research. For example, research on relational databases often uses synthetic data from the Wisconsin benchmark [DeW93] TPC C [TPCC] or TPC H [TPCH] and research on object oriented databases often uses synthetic data from the OO7 benchmark [CDN93] Synthetic data generators allow us to generate large volumes of data with well understood characteristics. We can easily vary the characteristics of the generated data by varying the input ....

....we generate. We provide a high degree of control over the characteristics of the generated XML data without requiring it to have a real world interpretation. Other examples of synthetic data that is commonly used in research on relational database systems include TPC C data [TPCC] and TPC H data [TPCH] Synthetic data that is more complex in structure than relational data includes the graph structured data from the OO7 benchmark for object oriented database systems [CDN93] and from the BUCKY benchmark for object relational database systems [CDN 97] Relational, object oriented, or ....

TPC benchmark H. Transaction Processing Performance Council (TPC). Available from http://www.tpc.org/.

....commonly accepted XML data generator for our community. 1 Introduction Synthetically generated data is very useful in evaluating and understanding new ideas in database research. For example, research on relational databases often uses synthetic data from the Wisconsin benchmark [DeW93] TPC C [TPCC] or TPC H [TPCH] and research on object oriented databases often uses synthetic data from the OO7 benchmark [CDN93] Synthetic data generators allow us to generate large volumes of data with well understood characteristics. We can easily vary the characteristics of the generated data by varying ....

....to the data that we generate. We provide a high degree of control over the characteristics of the generated XML data without requiring it to have a real world interpretation. Other examples of synthetic data that is commonly used in research on relational database systems include TPC C data [TPCC] and TPC H data [TPCH] Synthetic data that is more complex in structure than relational data includes the graph structured data from the OO7 benchmark for object oriented database systems [CDN93] and from the BUCKY benchmark for object relational database systems [CDN 97] Relational, ....

TPC benchmark C. Transaction Processing Performance Council (TPC). Available from http://www.tpc.org/.

.... Laboratory Page 6 11 23 99 updates) 17] and TPC D (decision support) 18] As with other industry benchmarks, these benchmarks continue to evolve [19] TPC A and TPC B were replaced by TPC C in June of 1995, and TPC D became obsolete in April of 1999, splitting into TPC H [20] and TPC R [21]. A new benchmark, TPC W [22] is under development to exercise web based transaction processing systems. The construction of the TPC family of benchmarks is open to industry participants with a stake in the outcome. Aside from the technical content of the benchmarks, members of the industry also ....

TPC Benchmark R Standard Specification, (Decision Support), Revision 1.0.1, Transaction Processing Performance Council.

.... Information Technology Laboratory Page 6 11 23 99 updates) 17] and TPC D (decision support) 18] As with other industry benchmarks, these benchmarks continue to evolve [19] TPC A and TPC B were replaced by TPC C in June of 1995, and TPC D became obsolete in April of 1999, splitting into TPC H [20] and TPC R [21] A new benchmark, TPC W [22] is under development to exercise web based transaction processing systems. The construction of the TPC family of benchmarks is open to industry participants with a stake in the outcome. Aside from the technical content of the benchmarks, members of the ....

TPC Benchmark H Standard Specification, (Decision Support), Revision 1.1.0, Transaction Processing Performance Council.

.... benchmarks: TPC A (on line transaction processing queries) 15] TPC B (update intensive database applications) 16] TPC C (a mixture of queries and Draft NIST Technical Report TR ANTD ANETS 111999 Information Technology Laboratory Page 6 11 23 99 updates) 17] and TPC D (decision support) [18]. As with other industry benchmarks, these benchmarks continue to evolve [19] TPC A and TPC B were replaced by TPC C in June of 1995, and TPC D became obsolete in April of 1999, splitting into TPC H [20] and TPC R [21] A new benchmark, TPC W [22] is under development to exercise web based ....

TPC Benchmark D Standard Specification, (Decision Support) Revision 2.1, Transaction Processing Performance Council.

.... four industry transaction processing benchmarks: TPC A (on line transaction processing queries) 15] TPC B (update intensive database applications) 16] TPC C (a mixture of queries and Draft NIST Technical Report TR ANTD ANETS 111999 Information Technology Laboratory Page 6 11 23 99 updates) [17], and TPC D (decision support) 18] As with other industry benchmarks, these benchmarks continue to evolve [19] TPC A and TPC B were replaced by TPC C in June of 1995, and TPC D became obsolete in April of 1999, splitting into TPC H [20] and TPC R [21] A new benchmark, TPC W [22] is under ....

TPC Benchmark C Standard Specification, Revision 3.4, Transaction Processing Performance Council, August 25, 1998.

....techniques have been devised to represent application mixes. The most notable synthetic, dynamic, multi threaded benchmarks we surveyed include four industry transaction processing benchmarks: TPC A (on line transaction processing queries) 15] TPC B (update intensive database applications) [16], TPC C (a mixture of queries and Draft NIST Technical Report TR ANTD ANETS 111999 Information Technology Laboratory Page 6 11 23 99 updates) 17] and TPC D (decision support) 18] As with other industry benchmarks, these benchmarks continue to evolve [19] TPC A and TPC B were replaced by TPC C ....

TPC Benchmark B Standard Specification, Revision 2.0, Transaction Processing Performance Council, June 7, 1994.

....typical applications. For that reason, other benchmark techniques have been devised to represent application mixes. The most notable synthetic, dynamic, multi threaded benchmarks we surveyed include four industry transaction processing benchmarks: TPC A (on line transaction processing queries) [15], TPC B (update intensive database applications) 16] TPC C (a mixture of queries and Draft NIST Technical Report TR ANTD ANETS 111999 Information Technology Laboratory Page 6 11 23 99 updates) 17] and TPC D (decision support) 18] As with other industry benchmarks, these benchmarks continue ....

TPC Benchmark A Standard Specification, Revision 2.0, Transaction Processing Performance Council, June 7, 1994.

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TPC Benchmark H. Transaction Processing Performance Council (TPC), 2002.

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TPC Benchmark H, Transaction Processing Performance Council, San Jose, CA 2002. http://www.tpc.org/tpch/spec/tpch2.1.0.pdf

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TPC. TPC Benchmark A. Standard Specification Report TPC-A-2.0-94, Transaction Processing Performance Council, San Francisco, California, June 1994.

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TPC Benchmark C Standard Specification, Transaction Processing Performance Council.

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Transaction Processing Performance Council (TPC). TPC Benchmark W (Web Commerce): Revision 1.8, February 2002.

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Transaction Processing Performance Council (TPC). TPC Benchmark R (Decision Support): Revision 2.0.0, 2002.

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Transaction Processing Performance Council (TPC). TPC Benchmark H (Decision Support): Revision 2.0.0, 2002.

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Transaction Processing Performance Council (TPC). TPC Benchmark C: Revision 5.1, December 2002.

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Transaction Processing Performance Council (TPC). TPC Benchmark D (Decision Support): Revision 2.12.0.1, 1998.

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Transaction Processing Performance Council (TPC). TPC Benchmark B: Revision 2.0, June 1994.

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Transaction Processing Performance Council (TPC). TPC Benchmark A: Revision 2.0, June 1994.

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