Results 11 to 20 of about 7,350 (305)
Online and Streaming Algorithms for Constrained k-Submodular Maximization
Proceedings of the AAAI Conference on Artificial IntelligenceConstrained k-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others. In many of these applications, datasets are large or decisions need to be made in an online manner, which motivates the development of efficient ...
Fabian Christian Spaeh +2 more
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Algorithms, 2020 We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired.
Rasoul Shafipour, Gonzalo Mateos
doaj +2 more sources
Low-Memory Algorithms for Online and W-Streaming Edge Coloring
CoRR, 2023 For edge coloring, the online and the W-streaming models seem somewhat orthogonal: the former needs edges to be assigned colors immediately after insertion, typically without any space restrictions, while the latter limits memory to sublinear in the input size but allows an edge's color to be announced any time after its insertion.
Prantar Ghosh, Manuel Stoeckl
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Online Comparison of Streaming Process Discovery Algorithms. [PDF]
, 2019 In the active field of process mining, several techniques have been proposed in various areas like process discovery and conformance checking. The integration of data stream mining techniques in process mining has gained popularity in recent years. The ProM framework that enables process mining with streaming data has been advanced to support event ...
Baskar, Kavya, Hassani, Marwan
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Simple Streaming Algorithms for Edge Coloring [PDF]
, 2022 We revisit the classical edge coloring problem for general graphs in the streaming model. In this model, the input graph is presented as a stream of edges, and the algorithm must report colors assigned to the edges in a streaming fashion, using a memory ...
Ansari, Mohammad +2 more
core +1 more source
Streaming Algorithms for Graph k-Matching with Optimal or Near-Optimal Update Time [PDF]
, 2021 We present streaming algorithms for the graph k-matching problem in both the insert-only and dynamic models. Our algorithms, while keeping the space complexity matching the best known upper bound, have optimal or near-optimal update time, significantly ...
Li, Qian +4 more
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Streaming algorithms for bin packing and vector scheduling [PDF]
, 2020 Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization.
Veselý, Pavel, Cormode, Graham
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Oblivious Algorithms for the Max-kAND Problem [PDF]
, 2023 Motivated by recent works on streaming algorithms for constraint satisfaction problems (CSPs), we define and analyze oblivious algorithms for the Max-kAND problem.
Singer, Noah G.
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Online algorithms for mining semi-structured data stream [PDF]
2002 IEEE International Conference on Data Mining, 2002. Proceedings., 2003 In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return ...
Tatsuya Asai +4 more
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Markov Boundary Learning With Streaming Data for Supervised Classification
IEEE Access, 2020 In this paper, we study the problem of Markov boundary (MB) learning with streaming data. A MB is a crucial concept in a Bayesian network (BN) and plays an important role in BN structure learning.
Chaofan Liu, Shuai Yang, Kui Yu
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