43 citations · 107 across the 27 of their papers we have counts for
27 papers
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…
GuP: Fast Subgraph Matching by Guard-based Pruning
Junya Arai, Yasuhiro Fujiwara, Makoto Onizuka
Subgraph matching, which finds subgraphs isomorphic to a query, is the key to information retrieval from data represented as a graph. To avoid redundant exploration in the data, ex…
A Simple and Scalable Graph Neural Network for Large Directed Graphs
Seiji Maekawa, Yuya Sasaki, Makoto Onizuka
Node classification is one of the hottest tasks in graph analysis. Though existing studies have explored various node representations in directed and undirected graphs, they have o…
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
Ryuichi Ito, Yuya Sasaki, Chuan Xiao +1
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applica…
NoSQL Schema Design for Time-Dependent Workloads
Yusuke Wakuta, Michael Mior, Teruyoshi Zenmyo +2
In this paper, we propose a schema optimization method for time-dependent workloads for NoSQL databases. In our proposed method, we migrate schema according to changing workloads,…
GNN Transformation Framework for Improving Efficiency and Scalability
Seiji Maekawa, Yuya Sasaki, George Fletcher +1
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our…
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi +2
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the m…
An Empirical Study of Personalized Federated Learning
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets o…
Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Yuya Sasaki, Junya Takayama, Juan Ramón Santana +3
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project i…
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
Seiji Maekawa, Koki Noda, Yuya Sasaki +1
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with l…
Journey of Migrating Millions of Queries on The Cloud
Taro L. Saito, Naoki Takezoe, Yukihiro Okada +11
Treasure Data is processing millions of distributed SQL queries every day on the cloud. Upgrading the query engine service at this scale is challenging because we need to migrate a…
Similarity Search on Computational Notebooks
Misato Horiuchi, Yuya Sasaki, Chuan Xiao +1
Computational notebook software such as Jupyter Notebook is popular for data science tasks. Numerous computational notebooks are available on the Web and reusable; however, searchi…
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…
FedMe: Federated Learning via Model Exchange
Koji Matsuda, Yuya Sasaki, Chuan Xiao +1
Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on…
GenCAT: Generating Attributed Graphs with Controlled Relationships between Classes, Attributes, and Topology
Seiji Maekawa, Yuya Sasaki, George Fletcher +1
Generating large synthetic attributed graphs with node labels is an important task to support various experimental studies for graph analysis methods. Existing graph generators fai…
AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Yuya Sasaki, Kei Harada, Shohei Yamasaki +1
Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality inform…
Smart City Data Analysis via Visualization of Correlated Attribute Patterns
Yuya Sasaki, Keizo Hori, Daiki Nishihara +9
Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and…
Fast Subgraph Matching by Exploiting Search Failures
Junya Arai, Makoto Onizuka, Yasuhiro Fujiwara +1
Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with ba…
Sequenced Route Query with Semantic Hierarchy
Yuya Sasaki, Yoshiharu Ishikawa, Yasuhiro Fujiwara +1
The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies…
Efficient Network Reliability Computation in Uncertain Graphs
Yuya Sasaki, Yasuhiro Fujiwara, Makoto Onizuka
Network reliability is an important metric to evaluate the connectivity among given vertices in uncertain graphs. Since the network reliability problem is known as #P-complete, exi…
Consistent and Flexible Selectivity Estimation for High-Dimensional Data
Yaoshu Wang, Chuan Xiao, Jianbin Qin +5
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many…
Language-aware Indexing for Conjunctive Path Queries
Yuya Sasaki, George Fletcher, Makoto Onizuka
Conjunctive path queries (CPQ) are one of the most frequently used queries for complex graph analysis. However, current graph indexes are not tailored to fully support the power of…
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
Yaoshu Wang, Chuan Xiao, Jianbin Qin +4
Due to the outstanding capability of capturing underlying data distributions, deep learning techniques have been recently utilized for a series of traditional database problems. In…
Making View Update Strategies Programmable - Toward Controlling and Sharing Distributed Data -
Yasuhito Asano, Soichiro Hidaka, Zhenjiang Hu +10
Views are known mechanisms for controlling access of data and for sharing data of different schemas. Despite long and intensive research on views in both the database community and…