Column Names Research Articles

KStore

KStore is a computer data structure based on the Phaneron of C. S. Peirce (Peirce, 1931-1958). This structure, called a Knowledge Store, KStore or simply K, is currently being developed as a storage engine to support BI data queries and analysis (Mazzagatti, 2007). The first Ks being constructed handle nominal data and record sequences of field/record data variables and their relationships. These rudimentary Ks are dynamic allowing real-time data processing, ad hoc queries and data compression, to facilitate data mining. This paper describes a next step in the development of the K structure, to record into the K structure, meta data associated with the field/record data, in particular the column or dimension names and a source indicator.

Schema Matching Using Interattribute Dependencies

Schema matching is one of the key challenges in information integration. It is a labor-intensive and time-consuming process. To alleviate the problem, many automated solutions have been proposed. Most of the existing solutions mainly rely upon textual similarity of the data to be matched. However, there exist instances of the schema matching problem for which they do not even apply. Such problem instances typically arise when the column names in the schemas and the data in the columns are opaque or very difficult to interpret. In our previous work [36] we proposed a two-step technique to address this problem. In the first step, we measure the dependencies between attributes within tables using an information-theoretic measure and construct a dependency graph for each table capturing the dependencies among attributes. In the second step, we find matching node pairs across the dependency graphs by running a graph matching algorithm. In our previous work, we experimentally validated the accuracy of the approach. One remaining challenge is the computational complexity of the graph matching problem in the second step. In this paper we extend the previous work by improving the second phase of the algorithm incorporating efficient approximation algorithms into the framework.

DNASTAT: a Pascal unit for the statistical analysis of DNA and protein sequences.

DNASTAT is a collection of Pascal routines for researchers who develop their own application programs for statistical analysis of DNA and protein sequences. Dynamic and file-based data structures allow users to process sets of sequences by simple loop control without limitations on the number of sequences and their individual sizes. This frees the programmer from potentially error-prone tasks like dynamic memory allocation and controlling array sizes. Sequences can be stored in databases along with biological and statistical attributes. Individual sequences can be accessed by column name and row number as with spread-sheets. DNASTAT allows large sets of sequences to be processed using a PC with standard configuration. Its small size, simplicity and free availability make it attractive to students of mathematical biology. Use of DNASTAT is illustrated by two sample programs that generate a database of coding regions from the GenBank entry of the tobacco chloroplast genome. A version of DNASTAT written in ANSI-C for PCs and Unix workstations is also available.

"Geolog System" for Computer and Manual Analysis of Geologic Data from Porphyry and Other Deposits

The Geolog System is a computer-oriented method of recording geological field observations, primarily from drill holes, in porphyry-type and other mineral deposits. The System also facilitates manual analysis and interpretation of the data.Data are compiled on GEOLOG, an 80-column format describing principal zone (cap, supergene, hypogene, fresh rock, fault), interval, rock type, fractures, alteration mineral assemblages, limonites, and ore mineral assemblages. Prescribed headings on GEOLOG are keyed to logical word abbreviations and simple codes to give concise, consistent, and readable descriptions. Because column names are easily modified and various codes and scales are available, GEOLOG is flexible; it therefore facilitates description of many types of deposit, accommodating a variety of logging techniques that may emphasize theoretical examination, routine survey, or rapid reconnaissance. Collecting data with Geolog System appears better than existing or standard logging techniques because information is recorded to be: keypunched directly; retrieved readily; consistent with respect to routine description of presence or absence of particular features; specific by use of prescribed headings, codes and scales; related to an existing porphyry deposit model. Time and effort involved in collecting drill data on GEOLOG are comparable to common logging procedures.Interpretation of the large amount of complex data on GEOLOG is aided by using a computer to provide manipulation of data and appropriate output. Because GEOLOG is keyed to recently published models of porphyry deposits that show ore location with respect to zoning of mode and degree of dispersion of mineralization, alteration mineral assemblages, and ore mineral assemblages, application of the System has important future use in predicting ore locations from even preliminary data.