Desktop Mapping: A Tool for Improving Small Business Marketing Anaysis and Customer Prospective

Moderately priced  personal  computer  based  spatial  analysis  systems  consisting  of  linked database,  spreadsheet,  and  desktop  mapping/GIS  programs   can  now  more  effectively  turn consumer and census data into useful information.  This paper  describes the operation of one such system - Microsoft Map - a feature  embedded  in Microsoft 's Excel spreadsheet.   Excel 's data-mapping capabilities let you plot  spreadsheets on thematic maps, making  it easier to see relationships  in your  data.  This  paper   implements  a  systematic  approach  for   applying consumer  data and spatial  marketing  techniques  to a small business, a photo  studio, for  trade area  growth   and   customer  mining.  Spotting  points   and  densities  of  sales for    the sampled customers provide   a foundation  for   understanding  the spatial  extent  of the customer  base. For the  small   business.  the  mapping   exercise   identified  purchasing   patterns  for    their  services, showing   those  areas   were  major  customers   reside.   Using  those  areas   identified  for    their services.  the company  can  begin  to plan   their strategy  to tap  into those  markets  and  hopefully increase  their sales.  It  is important  to note  that Excel 's mapping  abilities provide  a powerful and fertile   new  ground  for   data  analysis  experimentation.   S1nall   business  manager'   use  of spreadsheet   analysis,  combined   with  the  enhanced  presentation    mapping feature,    enable  the small  business person  to conduct  analyses  with even greater  detail

A machine learning approach for layout inference in spreadsheets

Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach deliver s very high accuracy bringing us a crucial step closer towards automatic table extraction.Peer ReviewedPostprint (published version