Search Results

Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group of <italic>k</italic> preferred locations among candidate sites to ...

Group link prediction is of both theoretical and practical significance since it can be used to analyze relationships between individuals and groups. However, obeying the homophily assumption, most of previous group link prediction methods suffer ...

• A novel GNN-based framework BiGLP is proposed for group link prediction.
• BiGLP leverages neighborhood information to learn the individual representations.
• BiGLP combines two types of relationships between nodes to learn the group ...

Mining multiple longest common subsequences (<italic>MLCS</italic>) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, ...

Hierarchical count histograms involve the publication of count statistics at various granularities based on a predefined hierarchy within a dimension table in a data warehouse. This task finds extensive applications in on-line analytical ...

Spatial temporal forecasting of urban sensors is essentially important for many urban systems, such as intelligent transportation and smart cities. However, due to the problem of hardware failure or network failure, there are some missing values or ...

By introducing intermediate states for metadata changes, shared-nothing databases are capable of making online, asynchronous schema changes. However, this method leads to delays in the deployment of new schema since it requires waiting for massive ...

Shared-nothing architecture has been widely adopted in rdbmss so that query can be processed in parallel and accelerated by scaling up the cluster horizontally on demand. However, in practice, skewed data distribution presents a great challenge in ...

The executor is an important component of a database. Typical executors that are applied in modern database systems follow either the VOLCANO model or Compiled model, each of which fits some scenarios but not all. Even the widely employed ...

• Enhance accuracy in choosing traditional or expression compilation iterative models.
• Efficiently generates ML feature vectors from query execution plans.
• Support for different workloads by using multiple Machine Learning models.

As one category of evolutionary algorithms, artificial bee colony (ABC) algorithms have many advantages such as few control parameters, simple structures and competitive performance. However, most ABC algorithms suffer from slow convergence and ...

• We propose an ABC-AS3 that performs well on both OPs with weak and OPs with strong correlations.
• We prove the rotation-variant/invariant property of the search strategies applied in the ABC-AS3 algorithm.
• We propose an AS3 ...

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the ...

Spatial temporal imputation is an important part of intelligent transportation system, and spatial temporal data missing also largely affects the performance of its downstream tasks. Spatial temporal data are affected by spatial, temporal, and ...

In various multi-party cooperations data stored on blockchains (i.e., on-chain data) should be decentralized consistent, verifiable, traceable, and immutable. Online analytical processing (OLAP) services are critical requirements in these applications. ...

Artificial bee colony (ABC) algorithms which are applied to various complex problems for their competitive performance and simple structures are a relatively popular paradigm of evolutionary algorithms (EAs). Although many variants ...

Temporal recommendation systems aim to suggest items to users at the optimal time. However, the significant imbalance of items in the training data poses a major challenge to predictive accuracy. Existing approaches attempt to alleviate this issue by ...

Spatio-temporal data are critical for intelligent systems, such as smart transportation and smart cities. However, due to sensor failure or power failure, the spatiotemporal data missing tends to have a big impact on downstream tasks. Meanwhile, ...

Long term dependency capture is essentially important for time series prediction and spatial–temporal forecasting. In recent years, many deep learning-based forecasting methods have been proposed, leading to rapid development in this area. We ...

Approximate nearest neighbor (ANN) search on high-dimensional data is a fundamental operation in many applications. In this paper, we study massive queries of ANN (MQ-ANN) search, which deals with a large number of queries simultaneously. To ...${\mathrm{}}^{\mathrm{}}$

Anomaly detection on attributed networks is widely used in online shopping, financial transactions, communication networks, and so on. However, most existing works trying to detect anomalies on attributed networks only considers a single kind of ...

Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of ...

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k > 1 facilities. ...