We present the DEVise toolkit designed for visual exploration of stream data. Data of this type are collected continuously from sources such as remote sensors, program traces, and the stock market. A typical application involves looking for correlations, which may not be precisely deened, by experimenting with graphical representations. This includes selectively comparing data from multiple sou...

Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item; it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams up...

The common approach to defining secure channels in the literature is to consider transportation of discrete messages provided via atomic encryption and decryption interfaces. This, however, ignores that many practical protocols (including TLS, SSH, and QUIC) offer streaming interfaces instead, moreover with the complexity that the network (possibly under adversarial control) may deliver arbitra...

We consider updates to an n-dimensional frequency vector of a data stream, that is, the vector f is updated coordinate-wise by means of insertions or deletions in any arbitrary order. A fundamental problem in this model is to recall the vector approximately, that is to return an estimate f̂ of f such that ∣f̂i − fi∣ < ∥f∥p, for every i = 1, 2, . . . , n, where is an accuracy parameter and p is th...

A data stream is a massive, continuous and rapid sequence of data elements. Mining data streams raises new problems for the data mining community about how to mine continuous high-speed data items that you can only have one look at. Due to this reason, traditional data mining approach is replaced by systems of some special characteristics, such as continuous arrival in multiple, rapid, time-var...

In this paper, a novel approach for building synopses is proposed by using a service and message-oriented architecture. The SaintEtiQ summarization system initially designed for very large stored databases, by its intrinsic features, is capable of dealing with the requirements inherent to the data stream environment. Its incremental maintenance of the output summaries and its scalability allows...

Many traffic analysis tasks are solved with tools that are developed in an ad-hoc, incremental, and cumbersome way instead of seeking systematic solutions that are easy to reuse and understand. The huge amount of data that has to be managed and analyzed together with the fact that many different analysis tasks are performed over a small set of different network trace formats, motivates us to st...

This paper describes Mortar, a distributed stream processing platform for building very large queries across federated systems (enterprises, grids, datacenters, testbeds). Nodes in such systems can be queried for distributed debugging, application control and provisioning, anomaly detection, and measurement. We address the primary challenges of managing continuous queries that have thousands of...

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that realtime query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, A...