Making large-scale support vector machine learning practical
Advances in kernel methods
Interactive methods for taxonomy editing and validation
Proceedings of the eleventh international conference on Information and knowledge management
LIPTUS: associating structured and unstructured information in a banking environment
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Top Issues from Contact Center Logs for Self Help Portals
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Business Intelligence from Voice of Customer
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Building re-usable dictionary repositories for real-world text mining
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Hierarchical service analytics for improving productivity in an enterprise service center
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ACM SIGMOD Record
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Data analytics tools and frameworks abound, yet rapid deployment of analytics solutions that deliver actionable insights from business data remains a challenge. The primary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business. The recent abundance of unstructured business data has thrown up new opportunities for analytics, but has also multiplied the deployment challenge, since interpretation of concepts derived from textual sources require a deep understanding of the business. In such a scenario, a managed service for analytics comes up as the best alternative. A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology. This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute. Also, the analytics needs to be repeatable, in that the delivered insights should not depend heavily on the expertise of specific analysts. These factors lead us to identify new areas that open up for KDD research in terms of 'time-to-insight' and repeatability for these analysts. We present our analytics framework in the form of a managed service offering for CRM analytics. We describe different analyst-centric tools using a case study from real-life engagements and demonstrate their effectiveness.