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Density-Based Clustering over an Evolving Data Stream with Noise
TLDR
A novel pruning strategy is designed based on these concepts, which guarantees the precision of the weights of the micro-clusters with limited memory, and demonstrates the effectiveness and efficiency of the method.
BiNE: Bipartite Network Embedding
TLDR
This work develops a representation learning method named BiNE, short for Bipartite Network Embedding, to learn the vertex representations for bipartite networks, and proposes a novel optimization framework by accounting for both the explicit and implicit relations in learning the vertices.
PeerDB: a P2P-based system for distributed data sharing
TLDR
The experimental results show that PeerDB can effectively exploit P2P technologies for distributed data sharing and is a full-fledge data management system that supports fine-grain content-based searching.
Tracking clusters in evolving data streams over sliding windows
TLDR
The exponential histogram is used to handle the in-cluster evolution, and the temporal cluster features represent the change of the cluster distribution, and a novel data structure, the Exponential Histogram of Cluster Features (EHCF) is proposed.
Dynamically maintaining frequent items over a data stream
TLDR
A new novel algorithm, called hCount, is proposed, which can handle both insertion and deletion of items with a much less memory space than the best reported algorithm, and is also superior in terms of precision, recall and processing time.
VBI-Tree: A Peer-to-Peer Framework for Supporting Multi-Dimensional Indexing Schemes
TLDR
This paper proposes a new Peer-to- Peer framework based on a balanced tree structure overlay, which can support extensible centralized mapping methods and query processingbased on a variety of multidimensional tree structures, including R-Tree, X- Tree, SSTree, and M-Tree.
Supporting multi-dimensional range queries in peer-to-peer systems
TLDR
An extensive performance study which evaluates ZNet against several recent proposals was conducted, and results show that ZNet possesses nearly all desirable properties, while others typically fail in one or another.
Finding Top-k Shortest Paths with Diversity
TLDR
It is proved that the KSPD problem is NP-hard and a greedy framework is proposed that supports a wide variety of path similarity metrics which are widely adopted in the literature and is able to efficiently solve the traditional KSP problem if no path similarity metric is specified.
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