135 research outputs found
Improved Algorithms for Time Decay Streams
In the time-decay model for data streams, elements of an underlying data set arrive sequentially with the recently arrived elements being more important. A common approach for handling large data sets is to maintain a coreset, a succinct summary of the processed data that allows approximate recovery of a predetermined query. We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions.
We also consider the exponential time decay model for k-median clustering, where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm. Our algorithm stores O(k log(h Delta)+h) points where h is the half-life of the decay function and Delta is the aspect ratio of the dataset. Our techniques extend to k-means clustering and M-estimators as well
Efficient representative subset selection over sliding windows
Representative subset selection (RSS) is an important tool for users to draw
insights from massive datasets. Existing literature models RSS as the
submodular maximization problem to capture the "diminishing returns" property
of the representativeness of selected subsets, but often only has a single
constraint (e.g., cardinality), which limits its applications in many
real-world problems. To capture the data recency issue and support different
types of constraints, we formulate dynamic RSS in data streams as maximizing
submodular functions subject to general -knapsack constraints (SMDK) over
sliding windows. We propose a \textsc{KnapWindow} framework (KW) for SMDK. KW
utilizes the \textsc{KnapStream} algorithm (KS) for SMDK in append-only streams
as a subroutine. It maintains a sequence of checkpoints and KS instances over
the sliding window. Theoretically, KW is
-approximate for SMDK. Furthermore, we propose a
\textsc{KnapWindowPlus} framework (KW) to improve upon KW. KW
builds an index \textsc{SubKnapChk} to manage the checkpoints and KS instances.
\textsc{SubKnapChk} deletes a checkpoint whenever it can be approximated by its
successors. By keeping much fewer checkpoints, KW achieves higher
efficiency than KW while still guaranteeing a
-approximate solution for SMDK. Finally, we
evaluate the efficiency and solution quality of KW and KW in real-world
datasets. The experimental results demonstrate that KW achieves more than two
orders of magnitude speedups over the batch baseline and preserves high-quality
solutions for SMDK over sliding windows. KW further runs 5-10 times
faster than KW while providing solutions with equivalent or even better
utilities.Comment: 26 pages, 9 figures, to appear in IEEE Transactions on Knowledge and
Data Engineering (TKDE). 201
Real-time influence maximization on dynamic social streams
Influence maximization (IM), which selects a set of users (called seeds)
to maximize the influence spread over a social network, is a fundamental
problem in a wide range of applications such as viral marketing and network
monitoring. Existing IM solutions fail to consider the highly dynamic nature of
social influence, which results in either poor seed qualities or long
processing time when the network evolves. To address this problem, we define a
novel IM query named Stream Influence Maximization (SIM) on social streams.
Technically, SIM adopts the sliding window model and maintains a set of
seeds with the largest influence value over the most recent social actions.
Next, we propose the Influential Checkpoints (IC) framework to facilitate
continuous SIM query processing. The IC framework creates a checkpoint for each
window slide and ensures an -approximate solution. To improve its
efficiency, we further devise a Sparse Influential Checkpoints (SIC) framework
which selectively keeps checkpoints for a sliding
window of size and maintains an
-approximate solution. Experimental results on
both real-world and synthetic datasets confirm the effectiveness and efficiency
of our proposed frameworks against the state-of-the-art IM approaches.Comment: An extended version of VLDB 2017 paper "Real-Time Influence
Maximization on Dynamic Social Streams", 14 page
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