Workshop Scope

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International Workshop on Big Data Science and Engineering on E-Commerce (BigEC’14) 

With a quickly expanding internet economy and rising rates of user engagement to e-commerce, big data science has become a key to gaining significant competitive advantages for leading E-commerce companies, such as Amazon, Rakuten, and Taobao. These E-commerce companies rely on Big Data to glean valuable, real-time insights that drive smarter, more profitable business decisions, such as personalization – using customers' preference and the actions they took to offer them special content and promotions; dynamic pricing – combining competitors’ price, product sales, regional preferences to dynamically decide the most competitive prices for products; predictive analytics – identifying sales patterns from history sales data to better predict inventory for avoiding products out-of-stock in the next go-around. These successful applications attract not only the research efforts but also the industry investments for big data science in e-commerce.

As the most profitable research topics, both scientists and engineers are increasingly interested in addressing a wide spectrum of challenges big data science in e-commerce. Generally speaking, big data in e-commerce are obtained from the following sources: the clickstream, the path to purchase, the referring website or search engine, the time spent on various locations of websites, the products viewed but not purchased, the browsing history, etc. Moreover, the third-party sources are usually used to as a part of big data in e-commerce, including the information of Facebook, Twitter, blog content, etc. While big data can yield extremely useful information, big data sciences in e-commerce presents not only the inherent three-V challenges (Volume, Velocity, and Variety) but also challenges related to business logics, including collecting data from various sources efficiently, interacting with customers in real-time, organizing huge amount unstructured data, evaluating effectiveness of campaign or promotion, detecting with the existence of cheating/tricky behaviors, targeting users from different sources, finding the correlations between , etc. 

BigEC'14 is to provide an informal and vibrant opportunity for researchers and industry practitioners to share their research positions, original research results and practical development experiences on specific challenges and emerging issues of big data science on e-commerce. As the workshop topics are focused and cohesive, participants can benefit from interaction with each other.