Web traffic analysis reveals the path users follow while browsing. E-commerce sites collect data tracing users’ trails to analyze the pages they are visiting most and in which order. User Experience Optimization: Clickstream helps understand the user’s mindset and improve sales through better engagement. This subsection describes the motivation usefulness of the clickstream analysis in practice. 1.1 Clickstream analysis application scenarios This work describes near real-time data storage and processing approaches to analyze streams of click data based on Apache Storm and Cassandra NoSQL datastore to bring insights into consumers’ browsing motifs and build the recently viewed products list at near equal to the pace the user continues to click on links. Our work is based on analyzing and predicting user clicks in real-time through a machine learning approach on a huge volume of heterogeneous data pool that requires a novel deployment model for stream processing frameworks and NoSQL datastore doing trade-off with Consistency, Availability, and Partition Tolerance (CAP) and also between throughput, latency, and correctness. Apache Storm is a popular real-time distributed processing framework allowing users in-flight processing on the inflow of data before it is even stored in the database. Introduced as a new category of open source project, a scalable stream processing paradigm, by Nathan Marz, creator for Apache storm while developing an ingestion pipeline for Twitter. ![]() Big Data Streaming is the data processing paradigm designed with infinite datasets in mind. In a Big Data real-time setting, instead of waiting for data to be gathered in its totality at a long periodic batch interval, the streaming analysis leads us to detect patterns and make informed conclusions based on them as data start arriving. Coupled with zero tolerance for data loss, the challenge gets even more daunting. Near real-time processing of a large data pool for creating unique personalized, contextual experiences need quick analysis of the inflow of data before it is even stored in the database of records. Users’ click to view items pile up to an enormous data volume compared to a tiny percentage of clicks converted to final checkouts. Mining browsing motifs to display personalized recommendations and near-real-time tracking of recently viewed products greatly enhances overall user experience and helps generate revenue. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.Į-Commerce sites track the consumers’ browsing patterns simultaneously in real-time and in batch mode. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. ![]() User-generated clickstream is first stored in a client site browser. This paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior.
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