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Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.

This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.



Chapter 1. An Introduction to Data Streams

In recent years, advances in hardware technology have facilitated new ways of collecting data continuously. In many applications such as network monitoring, the volume of such data is so large that it may be impossible to store the data on disk. Furthermore, even when the data can be stored, the volume of the incoming data may be so large that it may be impossible to process any particular record more than once. Therefore, many data mining and database operations such as classification, clustering, frequent pattern mining and indexing become significantly more challenging in this context.
In many cases, the data patterns may evolve continuously, as a result of which it is necessary to design the mining algorithms effectively in order to account for changes in underlying structure of the data stream. This makes the solutions of the underlying problems even more difficult from an algorithmic and computational point of view. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in data streams. The purpose of this chapter is to provide an overview of the organization of the stream processing and mining techniques which are covered in this book.
Charu C. Aggarwal

Chapter 2. On Clustering Massive Data Streams: A Summarization Paradigm

In recent years, data streams have become ubiquitous because of the large number of applications which generate huge volumes of data in an automated way. Many existing data mining methods cannot be applied directly on data streams because of the fact that the data needs to be mined in one pass. Furthermore, data streams show a considerable amount of temporal locality because of which a direct application of the existing methods may lead to misleading results. In this paper, we develop an efficient and effective approach for mining fast evolving data streams, which integrates the micro-clustering technique with the high-level data mining process, and discovers data evolution regularities as well. Our analysis and experiments demonstrate two important data mining problems, namely stream clustering and stream classification, can be performed effectively using this approach, with high quality mining results. We discuss the use of micro-clustering as a general summarization technology to solve data mining problems on streams. Our discussion illustrates the importance of our approach for a variety of mining problems in the data stream domain.
Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu

Chapter 3. A Survey of Classification Methods in Data Streams

With the advance in both hardware and software technologies, automated data generation and storage has become faster than ever. Such data is referred to as data streams. Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data. Most conventional data mining techniques have to be adapted to run in a streaming environment, because of the underlying resource constraints in terms of memory and running time. Furthermore, the data stream may often show concept drift, because of which adaptation of conventional algorithms becomes more challenging. One such important conventional data mining problem is that of classification. In the classification problem, we attempt to model the class variable on the basis of one or more feature variables. While this problem has been extensively studied from a conventional mining perspective, it is a much more challenging problem in the data stream domain. In this chapter, we will re-visit the problem of classification from the data stream perspective. The techniques for this problem need to be thoroughly re-designed to address the issue of resource constraints and concept drift. This chapter reviews the state-of-the-art techniques in the literature along with their corresponding advantages and disadvantages.
Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy

Chapter 4. Frequent Pattern Mining in Data Streams

Frequent pattern mining is a core data mining operation and has been extensively studied over the last decade. Recently, mining frequent patterns over data streams have attracted a lot of research interests. Compared with other streaming queries, frequent pattern mining poses great challenges due to high memory and computational costs, and accuracy requirement of the mining results.
In this chapter, we overview the state-of-art techniques to mine frequent patterns over data streams. We also introduce a new approach for this problem, which makes two major contributions. First, this one pass algorithm for frequent itemset mining has deterministic bounds on the accuracy, and does not require any out-of-core summary structure. Second, because the one pass algorithm does not produce any false negatives, it can be easily extended to a two pass accurate algorithm. The two pass algorithm is very memory efficient.
Ruoming Jin, Gagan Agrawal

Chapter 5. A Survey of Change Diagnosis Algorithms in Evolving Data Streams

An important problem in the field of data stream analysis is change detection and monitoring. In many cases, the data stream can show changes over time which can be used for understanding the nature of several applications. We discuss the concept of velocity density estimation, a technique used to understand, visualize and determine trends in the evolution of fast data streams. We show how to use velocity density estimation in order to create both temporal velocity profiles and spatial velocity profiles at periodic instants in time. These profiles are then used in order to predict three kinds of data evolution. Methods are proposed to visualize the changing data trends in a single online scan of the data stream, and a computational requirement which is linear in the number of data points. In addition, batch processing techniques are proposed in order to identify combinations of dimensions which show the greatest amount of global evolution. We also discuss the problem of change detection in the context of graph data, and illustrate that it may often be useful to determine communities of evolution in graph environments.
The presence of evolution in data streams may also change the underlying data to the extent that the underlying data mining models may need to be modified to account for the change in data distribution. We discuss a number of methods for micro-clustering which are used to study the effect of evolution on problems such as clustering and classification.
Charu C. Aggarwal

Chapter 6. Multi-Dimensional Analysis of Data Streams Using Stream Cubes

Large volumes of dynamic stream data pose great challenges to its analysis. Besides its dynamic and transient behavior, stream data has another important characteristic: multi-dimensionality. Much of stream data resides at a multidimensional space and at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes in some combination of dimensions. To discover high-level dynamic and evolving characteristics, one may need to perform multi-level, multi-dimensional on-line analytical processing (OLAP) of stream data. Such necessity calls for the investigation of new architectures that may facilitate on-line analytical processing of multi-dimensional stream data.
In this chapter, we introduce an interesting stream_cube architecture that effectively performs on-line partial aggregation of multi-dimensional stream data, captures the essential dynamic and evolving characteristics of data streams, and facilitates fast OLAP on stream data. Three important techniques are proposed for the design and implementation of stream cubes. First, a tilted time frame model is proposed to register time-related data in a multi-resolution model: The more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage requirements of time-related data and adapts nicely to the data analysis tasks commonly encountered in practice. Second, instead of materializing cuboids at all levels, two critical layers: observation layer and minimal interesting layer, are maintained to support routine as well as flexible analysis with minimal computation cost. Third, an efficient stream data cubing algorithm is developed that computes only the layers (cuboids) along a popular path and leaves the other cuboids for on-line, query-driven computation. Based on this design methodology, stream data cube can be constructed and maintained incrementally with reasonable memory space, computation cost, and query response time. This is verified by our substantial performance study.
Stream cube architecture facilitates online analytical processing of stream data. It also forms a preliminary structure for online stream mining. The impact of the design and implementation of stream cube in the context of stream mining is also discussed in the chapter.
Jiawei Han, Y. Dora Cai, Yixin Chen, Guozhu Dong, Jian Pei, Benjamin W. Wah, Jianyong Wang

Chapter 7. Load Shedding in Data Stream Systems

Systems for processing continuous monitoring queries over data streams must be adaptive because data streams are often bursty and data characteristics may vary over time. In this chapter, we focus on one particular type of adaptivity: the ability to gracefully degrade performance via “load shedding” (dropping unprocessed tuples to reduce system load) when the demands placed on the system cannot be met in full given available resources. Focusing on aggregation queries, we present algorithms that determine at what points in a query plan should load shedding be performed and what amount of load should be shed at each point in order to minimize the degree of inaccuracy introduced into query answers. We also discuss strategies for load shedding for other types of queries (set-valued queries, join queries, and classification queries).
Brian Babcock, Mayur Datar, Rajeev Motwani

Chapter 8. The Sliding-Window Computation Model and Results

The sliding-window model of computation is motivated by the assumption that, in certain data-stream processing applications, recent data is more useful and pertinent than older data. In such cases, we would like to answer questions about the data only over the last N most recent data elements (N is a parameter). We formalize this model of computation and answer questions about how much space and computation time is required to solve certain problems under the sliding-window model.
Mayur Datar, Rajeev Motwani

Chapter 9. A Survey of Synopsis Construction in Data Streams

The large volume of data streams poses unique space and time constraints on the computation process. Many query processing, database operations, and mining algorithms require efficient execution which can be difficult to achieve with a fast data stream. In many cases, it may be acceptable to generate approximate solutions for such problems. In recent years a number of synopsis structures have been developed, which can be used in conjunction with a variety of mining and query processing techniques in data stream processing. Some key synopsis methods include those of sampling, wavelets, sketches and histograms. In this chapter, we will provide a survey of the key synopsis techniques, and the mining techniques supported by such methods. We will discuss the challenges and tradeoffs associated with using different kinds of techniques, and the important research directions for synopsis construction.
Charu C. Aggarwal, Philip S. Yu

Chapter 10. A Survey of Join Processing in Data Streams

Given the fundamental role played by joins in querying relational databases, it is not surprising that stream join has also been the focus of much research on streams. Recall that relational (theta) join between two non-streaming relations R1 and R2, denoted R 1θ R 2, returns the set of all pairs (r 1, r 1), where r 1R 1, r 2R 2, and the join condition θ(r 1, r 2) evaluates to true. A straightforward extension of join to streams gives the following semantics (in rough terms): At any time t, the set of output tuples generated thus far by the join between two streams S 1 and S 2 should be the same as the result of the relational (non- streaming) join between the sets of input tuples that have arrived thus far in S 1 and S 2.
Junyi Xie, Jun Yang

Chapter 11. Indexing and Querying Data Streams

Online monitoring of data streams poses a challenge in many data-centric applications including network traffic management, trend analysis, web-click streams, intrusion detection, and sensor networks. Indexing techniques used in these applications have to be time and space efficient while providing a high quality of answers to user queries: (1) queries that monitor aggregates, such as finding surprising levels (“volatility” of a data stream), and detecting bursts, and (2) queries that monitor trends, such as detecting correlations and finding similar patterns. Data stream indexing becomes an even more challenging task, when we take into account the dynamic nature of underlying raw data. For example, bursts of events can occur at variable temporal modalities from hours to days to weeks. We focus on a multi-resolution indexing architecture. The architecture enables the discovery of “interesting” behavior online, provides flexibility in user query definitions, and interconnects registered queries for real-time and in-depth analysis.
Ahmet Bulut, Ambuj K. Singh

Chapter 12. Dimensionality Reduction and Forecasting on Streams

We consider the problem of capturing correlations and finding hidden variables corresponding to trends on collections of time series streams. Our proposed method, SPIRIT, can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing.
Spiros Papadimitriou, Jimeng Sun, Christos Faloutsos

Chapter 13. A Survey of Distributed Mining of Data Streams

With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.
Srinivasan Parthasarathy, Amol Ghoting, Matthew Eric Otey

Chapter 14. Algorithms for Distributed Data Stream Mining

The field of Distributed Data Mining (DDM) deals with the problem of analyzing data by paying careful attention to the distributed computing, storage, communication, and human-factor related resources. Unlike the traditional centralized systems, DDM offers a fundamentally distributed solution to analyze data without necessarily demanding collection of the data to a single central site. This chapter presents an introduction to distributed data mining for continuous streams. It focuses on the situations where the data observed at different locations change with time. The chapter provides an exposure to the literature and illustrates the behavior of this class of algorithms by exploring two very different types of techniques—one for the peer-to-peer and another for the hierarchical distributed environment. The chapter also briefly discusses several different applications of these algorithms.
Kanishka Bhaduri, Kamalika Das, Krishnamoorthy Sivakumar, Hillol Kargupta, Ran Wolff, Rong Chen

Chapter 15. A Survey of Stream Processing Problems and Techniques in Sensor Networks

Sensor networks comprise small, low-powered and low-cost sensing devices that are distributed over a field to monitor a phenomenon of interest. The sensor nodes are capable of communicating their readings, typically through wireless radio. Sensor nodes produce streams of data, that have to be processed in-situ, by the node itself, or to be transmitted through the network, and analyzed offline. In this chapter we describe recently proposed, efficient distributed techniques for processing streams of data collected with a network of sensors.
Sharmila Subramaniam, Dimitrios Gunopulos


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