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Keyword Search in Spatial Databases: Towards Searching by Document
- D. Zhang, Y. Chee, Anirban Mondal, A. Tung, M. Kitsuregawa
- Computer Science, EconomicsIEEE 25th International Conference on Data…
- 29 March 2009
This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query, which aims to find the spatially closest tuples which match m user-specified keywords, and introduces a new index called the bR*-tree, which is an extension of the R-tree.
Fast and Exact Top-k Search for Random Walk with Restart
- Y. Fujiwara, M. Nakatsuji, Makoto Onizuka, M. Kitsuregawa
- Computer ScienceProc. VLDB Endow.
- 31 January 2012
K-dash is a solution to find nodes that have top-k highest proximities for a given node based on two ideas: it computes the proximity of a selected node efficiently by sparse matrices, and it skips unnecessary proximity computations when searching for the top- k nodes.
Creating a Web community chart for navigating related communities
This paper proposes a technique to create a web community chart, that connects related web communities, from thousands of seed pages, and shows that the algorithm can be used for creating the chart by applying the algorithm to each seed, then using similarities of the results to classify seeds into clusters and to deduce their relationships.
Hash based parallel algorithms for mining association rules
- T. Shintani, M. Kitsuregawa
- Computer ScienceFourth International Conference on Parallel and…
- 1 December 1996
Four parallel algorithms for mining association rules on shared nothing parallel machines to improve its performance are proposed and the best algorithm, HPA-ELD, attains good linearity on speedup ratio and is effective for handling skew.
Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents
The key idea is to develop the structural clues so that it achieves extremely high precision at the cost of recall, and build lexicon from the extracted polar sentences.
Hash-Partitioned Join Method Using Dynamic Destaging Strategy
This paper proposes a strategy in which the destaging buckets are selected dynamically, instead of a static decision of them during the split phase, and this method can be applied in many cases, which are unsuited to traditional methods.
Learning to Describe Unknown Phrases with Local and Global Contexts
A neural description model that consists of two context encoders and a description decoder that appropriately takes important clues from both local and global contexts is proposed.
P2PR-Tree: An R-Tree-Based Spatial Index for Peer-to-Peer Environments
The results of the performance evaluation demonstrate that it is indeed practically feasible to share spatial data in a P2P system and that P2PR-tree is able to outperform MC-Rtree significantly.
Dynamic Adaptation Strategies for Long-Term and Short-Term User Profile to Personalize Search
An adaptive scheme to learn the changes of user preferences from click-history data, and a novel rank mechanism to bias the search results of each user are introduced.
LAPIN: Effective Sequential Pattern Mining Algorithms by Last Position Induction for Dense Databases
This work proposes a series of novel algorithms, called the LAst Position INduction (LAPIN) sequential pattern mining, which is based on the simple idea that the last position of an item, α is the key to judging whether or not a frequent k-length sequential pattern can be extended to be a frequent (k+1)-length pattern by appending the item α to it.