A review on time series data mining
Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …
scientific and financial applications. A time series is a collection of observations made …
A survey on learning from data streams: current and future trends
Nowadays, there are applications in which the data are modeled best not as persistent
tables, but rather as transient data streams. In this article, we discuss the limitations of …
tables, but rather as transient data streams. In this article, we discuss the limitations of …
[كتاب][B] Data mining: concepts and techniques
Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …
methods for mining patterns, knowledge, and models from various kinds of data for diverse …
Synopses for massive data: Samples, histograms, wavelets, sketches
Abstract Methods for Approximate Query Processing (AQP) are essential for dealing with
massive data. They are often the only means of providing interactive response times when …
massive data. They are often the only means of providing interactive response times when …
Similarity estimation techniques from rounding algorithms
(MATH) A locality sensitive hashing scheme is a distribution on a family \F of hash functions
operating on a collection of objects, such that for two objects x, y, Pr h εF h (x)= h (y)= sim (x …
operating on a collection of objects, such that for two objects x, y, Pr h εF h (x)= h (y)= sim (x …
An improved data stream summary: the count-min sketch and its applications
We introduce a new sublinear space data structure—the count-min sketch—for summarizing
data streams. Our sketch allows fundamental queries in data stream summarization such as …
data streams. Our sketch allows fundamental queries in data stream summarization such as …
Models and issues in data stream systems
In this overview paper we motivate the need for and research issues arising from a new
model of data processing. In this model, data does not take the form of persistent relations …
model of data processing. In this model, data does not take the form of persistent relations …
Data streams: Algorithms and applications
In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …
the input. Algorithms have to work with one or few passes over the data, space less than …
Approximate frequency counts over data streams
Publisher Summary This chapter presents algorithms for computing frequency counts
exceeding a user-specified threshold over data streams. The algorithms are simple and …
exceeding a user-specified threshold over data streams. The algorithms are simple and …
Maintaining stream statistics over sliding windows
We consider the problem of maintaining aggregates and statistics over data streams, with
respect to the last N data elements seen so far. We refer to this model as the sliding window …
respect to the last N data elements seen so far. We refer to this model as the sliding window …