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While big data has been a topic of research and industry activity, much of it has been focused on unstructured data such as web logs, web crawl data, and social media data. One area which has received less attention but offers significant opportunities is enterprise big data. As companies drive towards leveraging analytics to create new value, they are faced with one of the most daunting challenges: How can we link data from hundreds of business processes, tens of businesses, and combine relevant enterprise data with external data to enable novel analytical insights? Consequently, Enterprise Big Data Semantics, Analytics and Modeling (EBDSAM) is an emerging area of research. This workshop will share key challenges in EBDSAM, novel approaches to solutions, and new business challenges that can be addressed by big data semantic and analytics modeling.

Invited Talk 1: Semantic Search

by Dr. Ricardo Baeza-Yates, IEEE fellow, ACM fellow, Chief Technology Officer of NTENT.

Semantic search lies in the cross roads of information retrieval and natural language processing, and is the current frontier of search technology. In this talk we will cover 1. extraction of the entities and concepts from documents, 2. predicting the intention behind the query, i.e., semantic query understanding, and 3. machine learning in semantic ranking.

Full abstract and speaker bio [here]({% link ricardo_bio.md %}).

Invited Talk 2: Innovation in Big Data Analytics

by Dr. Eva K Lee, Professor at School of Industrial and Systems Engineering, Georgia Institute of Technology, Director of Center for Operations Research in Medicine and HealthCare.

Risk and decision models and predictive analytics have long been cornerstones for advancement in industrial, government, and military applications. In particular, multi-source data system modeling and big data analytics and technologies play an increasingly important role in modern business enterprise. Many problems arising in these domains can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this talk, we will share some of our successes in healthcare, defense, and service sector applications through innovation in predictive and big data analytics.

Speaker bio available [here]({% link eva_bio.md %}), and speaker's website here.

Workshop Schedule

Click [here]({{ site.baseurl }}/assets/WorkshopSchedule_v8.pdf) for the tentative workshop schedule (there may be minor changes in the future).

Workshop Topics

Workshop papers can fall into any of the following categories involving exploiting of enterprise big data and / or enterprise applications:

  • Natural Language Processing in business analytics
  • Use of big data to estimate employee value
  • Machine learning for financial planning
  • Workforce optimization and skill mix strategy - use of big data to understand which teams or types of employees complement each other and work well together
  • Internal company recommendation engines
  • Selection of products and offerings to maximize profit
  • Salesforce optimization
  • Initiatives that use big data to understand and develop employee skillsets
  • Use of big data to determine when/how to use various marketing and sales channels
  • Challenges in organizing enterprise big data from a variety of internal sources
  • Challenges of combining internal organization data with external data (for example, dealing with unique internal taxonomies)
  • Additional related categories not covered above

Import Dates

  • This workshop will be on Dec 11 2017
  • The main conference is Dec 11-14 2017

Program Chairs

  • Michael Peran, IBM

Program Committee Members

  • Anshul Sheopuri, IBM
  • Xin Xu, Clari
  • Jonathan Debusk, IBM
  • Ben Zweig, IBM
  • Brian Johnston, IBM
  • Emily Beliveau, IBM
  • Charlie Wang, IBM
  • Rachel Rosenfeld, IBM