Section: Research Program

Data Analytics

Data analytics refers to a set of techniques to draw conclusions through data examination. It involves data mining, statistics, and data management. Data mining provides methods to discover new and useful patterns from very large datasets. These patterns may take different forms, depending on the end-user's request, such as:

• Frequent itemsets and association rules. In this case, the data is usually a table with a high number of rows and the data mining algorithm extracts correlations between column values. This problem was first motivated by commercial and marketing purposes (e.g. discovering frequent correlations between items bought in a shop, which could help selling more). A typical example of frequent itemset from a sensor network in a smart building would say that “in 20% rooms, the door is closed, the room is empty, and lights are on.”

• Frequent sequential pattern extraction. This problem is very similar to frequent itemset discovery but considering the order between. In the smart building example, a frequent sequence could say that “in 40% of rooms, lights are on at time i, the room is empty at time i+j and the door is closed at time i+j+k”. Discovering frequent sequences has become critical in marketing, as well as in security (e.g. detecting network intrusions), in web usage analysis and any domain where data come in a specific order, typically given by timestamps.

• Clustering. The goal of clustering is to group together similar data while ensuring that dissimilar data will not be in the same cluster. In our example of smart buildings, we could find clusters of rooms, where offices will be in one category and copy machine rooms in another because of their differences (hours of people presence, number of times lights are turned on/off, etc.).

One main problem in data analytics is to deal with data streams. Existing methods have been designed for very large data sets where complex algorithms from artificial intelligence were not efficient because of data size. However, we now must deal with data streams, sequences of data events arriving at high rate, where traditional data analytics techniques cannot complete in real-time, given the infinite data size. In order to extract knowledge from data streams, the data mining community has investigated approximation methods that could yield good result quality.