3 citations · 9 across the 16 of their papers we have counts for
16 papers
Duration-constrained Interval Joins
Naoya Ehara, Daichi Amagata
Many databases, including temporal, uncertain, spatial, and trajectory databases, use interval data, and interval joins are among the most frequently used operators. Many studies p…
Simple and Fast Algorithm for Graph-based Filtered Approximate Nearest Neighbor Search (Full Version)
Reon Uemura, Keito Kido, Daichi Amagata
It has been common to represent many objects as high-dimensional vectors due to the proliferation of machine learning-based embedding techniques. One of the most important function…
How Should We Evaluate Data Deletion in Graph-Based ANN Indexes?
Tomohiro Yamashita, Daichi Amagata, Yusuke Matsui
Approximate Nearest Neighbor Search (ANNS) has recently gained significant attention due to its many applications, such as Retrieval-Augmented Generation. Such applications require…
Random Sampling over Spatial Range Joins
Daichi Amagata
Spatial range joins have many applications, including geographic information systems, location-based social networking services, neuroscience, and visualization. However, joins inc…
Approximate Reverse -Ranks Queries in High Dimensions
Daichi Amagata, Kazuyoshi Aoyama, Keito Kido +1
Many objects are represented as high-dimensional vectors nowadays. In this setting, the relevance between two objects (vectors) is usually evaluated by their inner product. Recentl…
How to Mine Potentially Popular Items? A Reverse MIPS-based Approach
Daichi Amagata, Kazuyoshi Aoayama, Keito Kido +1
The -MIPS ( Maximum Inner Product Search) problem has been employed in many fields. Recently, its reverse version, the reverse -MIPS problem, has been proposed. Given an i…
Independent Range Sampling on Interval Data (Longer Version)
Daichi Amagata
Many applications require efficient management of large sets of intervals because many objects are associated with intervals (e.g., time and price intervals). In such interval mana…
Efficient Algorithms for Top-k Stabbing Queries on Weighted Interval Data (Full Version)
Daichi Amagata, Junya Yamada, Yuchen Ji +1
Intervals have been generated in many applications (e.g., temporal databases), and they are often associated with weights, such as prices. This paper addresses the problem of proce…
Fast Algorithm for Embedded Order Dependency Validation (Extended Version)
Alejandro Ramos, Takuya Uemura, Daichi Amagata +2
Order Dependencies (ODs) have many applications, such as query optimization, data integration, and data cleaning. Although many works addressed the problem of discovering OD (and i…
Learned spatial data partitioning
Keizo Hori, Yuya Sasaki, Daichi Amagata +2
Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we f…
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations
Zhi Li, Daichi Amagata, Yihong Zhang +4
Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To ad…
Learned k-NN Distance Estimation
Daichi Amagata, Yusuke Arai, Sumio Fujita +1
Big data mining is well known to be an important task for data science, because it can provide useful observations and new knowledge hidden in given large datasets. Proximity-based…
Fast Density-Peaks Clustering: Multicore-based Parallelization Approach
Daichi Amagata, Takahiro Hara
Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. T…
Fast and Exact Outlier Detection in Metric Spaces: A Proximity Graph-based Approach
Daichi Amagata, Makoto Onizuka, Takahiro Hara
Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, an…
Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?
Daichi Amagata, Takahiro Hara
The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usu…
Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
Shohei Tsuruoka, Daichi Amagata, Shunya Nishio +1
Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generate…