Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Computer Hardware Data Set
Download: Data Folder, Data Set Description

Abstract: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.

Data Set Characteristics:  

Multivariate

Number of Instances:

209

Area:

Computer

Attribute Characteristics:

Integer

Number of Attributes:

9

Date Donated

1987-10-01

Associated Tasks:

Regression

Missing Values?

No

Number of Web Hits:

361854


Source:

Creator:

Phillip Ein-Dor and Jacob Feldmesser
Ein-Dor: Faculty of Management
Tel Aviv University; Ramat-Aviv;
Tel Aviv, 69978; Israel

Donor:

David W. Aha (aha '@' ics.uci.edu) (714) 856-8779


Data Set Information:

The estimated relative performance values were estimated by the authors using a linear regression method. See their article (pp 308-313) for more details on how the relative performance values were set.


Attribute Information:

1. vendor name: 30
(adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec,
dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson,
microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry,
sratus, wang)
2. Model Name: many unique symbols
3. MYCT: machine cycle time in nanoseconds (integer)
4. MMIN: minimum main memory in kilobytes (integer)
5. MMAX: maximum main memory in kilobytes (integer)
6. CACH: cache memory in kilobytes (integer)
7. CHMIN: minimum channels in units (integer)
8. CHMAX: maximum channels in units (integer)
9. PRP: published relative performance (integer)
10. ERP: estimated relative performance from the original article (integer)


Relevant Papers:

Ein-Dor and Feldmesser (CACM 4/87, pp 308-317)

Kibler,D. & Aha,D. (1988). Instance-Based Prediction of Real-Valued Attributes. In Proceedings of the CSCSI (Canadian AI) Conference.
[Web Link]


Papers That Cite This Data Set1:

Dan Pelleg. Scalable and Practical Probability Density Estimators for Scientific Anomaly Detection. School of Computer Science Carnegie Mellon University. 2004. [View Context].

Yongge Wang. A New Approach to Fitting Linear Models in High Dimensional Spaces. Alastair Scott (Department of Statistics, University of Auckland). [View Context].


Citation Request:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML