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CONCLUSION

This study will be helpful for small and medium size enterprise to take their business decision. To start any SME (Small and Mid-sized Enterprise) identification of probable customers and their preferences are crucial because the area of operation for any SME is quite limited in comparison to any big enterprise. As SME cannot spent too money on the survey as compare to big enterprise to find the customer behavior about the product, so this research will be helpful for the SME and new organization to take sound business decision. As in current era customer detection and attraction can be done through social media that requires on relational techniques to operate and handle data. Facebook and google are the best way to publicize once idea and to know the current trends. So these social sites demands to have graph based technique to work with them. This study will help to manage semi structure data of an organization. Different semantic techniques are explain in this chapter will be helpful while selecting appropriate technique. SSDM technique is explained using food purchase habit and this technique will be helpful for the management of semantic data and for the conversion of relational data. Apart from this user can directly insert unstructured or semi unstructured data into the derive solution. SSDM approach allows unstructured data to be the part of domain ontology. AS a final output of SSDM we got a semantic database on which we can query the data using either SPARQL queries or using direct inference. Obtained domain ontology can be explore using data logics or graphviz (owlviz). This work can be used in food chain, transport system, sale and purchase organizations. In last we can conclude that this chapter will be help full for the management of SME’s semi structured data using semantic techniques and act as easy way for detection of probable customer.

 
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