In this talk we will give a very brief overview of Flipkart, highlighting the major milestones in its journey so far and some latest market size numbers. We will enumerate some of the important data science challenges in an e-commerce company like ours. Finally we will cover one of the problems, demand forecasting, in some details to highlight some of our recent work: Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. We propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand

The subject matter of this document generally relates to reducing noise in aggregated data using frequency analysis. In some implementations, a system for reducing data noise using frequency analysis includes a data storage device that stores content and a network association processor in data communication with the data storage device. The network association processor aggregates, for a given group, content of one or more additional groups that each have overlapping members with the given group. The network association processor reduces noise in the aggregated content of the one or more additional groups using frequency analysis by determining, for each portion of content in the aggregated content, a frequency of occurrence of the portion of content within the aggregated content and filtering, from the aggregated content, each portion of content that has a frequency of occurrence that is less than a threshold.

Traditional database management systems are best equipped to run one-time queries over finite stored data sets. However, many modern applications such as network monitoring, financial analysis, manufacturing, and sensor networks require long-running, or continuous, queries over continuous unbounded streams of data. In the STREAM project at Stanford, we are investigating data management and query processing for this class of applications. As part of the project we are building a general-purpose prototype Data Stream Management System (DSMS), also called STREAM, that supports a large class of declarative continuous queries over continuous streams and traditional stored data sets. The STREAM prototype targets environments where streams may be rapid, stream characteristics and query loads may vary over time, and system resources may be limited.

We present some results related to small space computation over sliding windows in the data-stream model. Most research in the data-stream model, including results presented in some of the other chapters, assume that all data elements seen so far in the stream are equally important and synopses, statistics or models that are built should reflect the entire data set. However, for many applications this assumption is not true, particularly those that ascribe more importance to recent data items. One way to discount old data items and only consider recent ones for analysis is the sliding-window model: Data elements arrive at every instant; each data element expires after exactly N time steps; and, the portion of data that is relevant to gathering statistics or answering queries is the set of last N elements to arrive. The sliding window refers to the window of active data elements at a given time instant and window size refers to N. This chapter presents a general technique, called the Exponential Histogram (EH) technique, that can be used to solve a wide variety of problems in the sliding-window model; typically problems that require us to maintain statistics. We will showcase this technique through solutions to basic counting problems, as well as other applications.