A Survey of Distributed Data Stream Processing Frameworks <s> 12) SUPPORT FOR ANALYTICS <s> 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. <s> BIB001 </s> A Survey of Distributed Data Stream Processing Frameworks <s> 12) SUPPORT FOR ANALYTICS <s> SAMOA (SCALABLE ADVANCED MASSIVE ONLINE ANALYSIS) is a platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. samoa is written in Java, is open source, and is available at http://samoa-project.net under the Apache Software License version 2.0. <s> BIB002
Besides the regular search and data storage systems, another desirable feature of DSPEs is support for analytical and continuous queries. The usual pipeline for mining and modelling data involves taking a sample or a snapshot from production data, cleaning and pre-processing the data, training and evaluating a model, and finally deploying the model to a production system. Most implementations of this traditional data mining pipeline such as WEKA and R are unable to cope with web-scale datasets. Hence the evolution of distributed data mining systems such as Mahout  . However, most data are now generated in the form of a stream and requires streaming models BIB001 . This involves analyzing data in near real-time or as soon as it arrives into the system. Most implementations of traditional stream processing systems such as Massive Online Analysis (MOA) are limited by the memory and bandwidth of a single machine BIB002 . Hence the evolution of distributed data stream mining systems such as the Scalable Advanced Massive Online Analysis (SAMOA), which enables the development of distributed streaming algorithms and executing them on multiple DSPEs BIB002 .