Space-bounded Extreme Aggregation of Data Stream over Time-based Sliding Window

Graph coloring is one of the most famous computational problems with applications in a wide range of areas such as planning and scheduling, resource allocation, and pattern matching. So far coloring problems are mostly studied on static graphs, which often stand in stark contrast to practice where data is inherently dynamic and subject to discrete changes over time. A temporal graph is a graph whose edges are assigned a set of integer time labels, indicating at which discrete time steps the edge is active. In this paper we present a natural temporal extension of the classical graph coloring problem. Given a temporal graph and a natural number ∆, we ask for a coloring sequence for each vertex such that (i) in every sliding time window of ∆ consecutive time steps, in which an edge is active, this edge is properly colored (i.e. its endpoints are assigned two different colors) at least once during that time window, and (ii) the total number of different colors is minimized. This sliding window temporal coloring problem abstractly captures many realistic graph coloring scenarios in which the underlying network changes over time, such as dynamically assigning communication channels to moving agents. We present a thorough investigation of the computational complexity of this temporal coloring problem. More specifically, we prove strong computational hardness results, complemented by efficient exact and approximation algorithms. Some of our algorithms are linear-time fixed-parameter tractable with respect to appropriate parameters, while others are asymptotically almost optimal under the Exponential Time Hypothesis (ETH).

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KIDS:K-anonymization data stream base on sliding window

2010 2nd International Conference on Future Computer and Communication ◽

10.1109/icfcc.2010.5497420 ◽

2010 ◽

Cited By ~ 5

Author(s):

Junwei Zhang ◽

Jing Yang ◽

Jianpei Zhang ◽

Yongbin Yuan

Keyword(s):

Data Stream ◽

Sliding Window

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Estimation of the Number of Distinct Values over Data Stream Based on Compound Sliding Window

Journal of Software ◽

10.4304/jsw.8.1.19-24 ◽

2013 ◽

Vol 8 (1) ◽

Author(s):

Yingli Zhong ◽

Jinghua Zhu ◽

Meirui Ren ◽

Yan Yang

Keyword(s):

Data Stream ◽

Sliding Window

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CACHE HIERARCHY INSPIRED COMPRESSION: A NOVEL ARCHITECTURE FOR DATA STREAMS

Journal of IT in Asia ◽

10.33736/jita.54.2007 ◽

2016 ◽

Vol 2 (1) ◽

pp. 39-52

Author(s):

G. Holmes ◽

B. Pfahringer ◽

R. Kirkby

Keyword(s):

Data Streams ◽

Incremental Learning ◽

Data Stream ◽

Main Idea ◽

Replacement Policy ◽

Cache Hierarchies ◽

Meta Level ◽

Web Cache ◽

General Architecture ◽

Over Time

We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. We present the general architecture for such a system and an application to classification. This architecture is an instance of the general wrapper idea allowing us to reuse standard batch learning algorithms in an inherently incremental learning environment. By artificially generating data sources we demonstrate that a hierarchy containing a mixture of models is able to adapt over time to the source of the data. In these experiments the hierarchies use an elementary performance based replacement policy and unweighted voting for making classification decisions.

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