Chapter 2 – Data Collection

Industrial planners and managers need to understand the dynamics of complete supply chain, i.e., stocks, operations, infrastructure, communities, and individuals involved in the sector to set policy and manage industrial assets. Data collection and analysis, for example, can provide information on how industry is likely to respond to different policies. Constraints on production and development of new factories can be identified. Prices and cost changes in the manufacturing facilities can be assessed. Stocks likely to receive increased levels of exploitation may be identified before resource levels drop to a crisis point.

[1]  Chris Clifton,et al.  SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks , 2000, Data Knowl. Eng..

[2]  Shimon Even,et al.  Efficient Generation of Optimal Prefix Code: Equiprobable Words Using Unequal Cost Letters , 1975, JACM.

[3]  Kurt Mehlhorn,et al.  An efficient algorithm for constructing nearly optimal prefix codes , 1980, IEEE Trans. Inf. Theory.

[4]  Stefano Spaccapietra,et al.  Issues and approaches of database integration , 1998, CACM.

[5]  Frank Rubin,et al.  Experiments in text file compression , 1976, CACM.

[6]  Ben Varn,et al.  Optimal Variable Length Codes (Arbitrary Symbol Cost and Equal Code Word Probability) , 1971, Inf. Control..

[7]  D. Huffman.  A Method for the Construction of Minimum-Redundancy Codes , 1952 .

[8]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[9]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[10]  Richard M. Karp,et al.  Minimum-redundancy coding for the discrete noiseless channel , 1961, IRE Trans. Inf. Theory.

[11]  Edgar N. Gilbert,et al.  Codes based on inaccurate source probabilities , 1971, IEEE Trans. Inf. Theory.

[12]  Daniel S. Hirschberg,et al.  Data compression , 1987, CSUR.

[13]  Robert E. Tarjan,et al.  A Locally Adaptive Data , 1986 .

[14]  Ian H. Witten,et al.  Modeling for text compression , 1989, CSUR.

[15]  Salvatore J. Stolfo,et al.  Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem , 1998, Data Mining and Knowledge Discovery.

[16]  Ralph M. Krause,et al.  Channels Which Transmit Letters of Unequal Duration , 1962, Inf. Control..

[17]  Terry A. Welch,et al.  A Technique for High-Performance Data Compression , 1984, Computer.

[18]  Vipul Kashyap,et al.  Semantic and schematic similarities between database objects: a context-based approach , 1996, The VLDB Journal.