Bus-OLAP: A Data Management Model for Non-on-Time Events Query Over Bus Journey Data

Duan, Lei; Pang, Tinghai; Nummenmaa, Jyrki; Zuo, Jian; Zhang, Peng; Tang, Changjie

doi:10.1007/s41019-018-0061-9

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Online analytical processing (OLAP) is a core functionality in data management and analytics systems [33]. The performance of OLAP is crucial for many applications that need to use OLAP to make online decisions, e.g., business intelligence.…”

Section: Introductionmentioning

confidence: 99%

Approximate Query Processing: What is New and Where to Go?

2018

Data Sci. Eng.

112

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Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications.

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Section: Introductionmentioning

confidence: 99%

Approximate Query Processing: What is New and Where to Go?

2018

Data Sci. Eng.

112

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“…Secondly, even though various error-based metrics have been proposed to measure the compression quality, the usability of the reduced trajectory data still remains an unresolved question. In this paper, we make a reasonable assumption that in a trajectory database management system, the compression algorithms are located in the storage layer to support high-level query processing [13,46] or pattern mining tasks [14,44,12]. Hence, data usability is also a key factor for compression quality measurement.…”

Section: Introductionmentioning

confidence: 99%

Trajectory simplification

et al. 2018

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The ubiquitousness of GPS sensors in smart-phones, vehicles and wearable devices has enabled the collection of massive volumes of trajectory data from tracing moving objects. Consequently, an unprecedented scale of timestamped GPS data has been generated and posed an urgent demand for an effective storage mechanism for trajectory databases. The mainstream compression technique is called trajectory simplification, that finds a subsequence to approximate the original trajectory and attempts to minimize the information loss under a distance measure. Even though various simplification algorithms have been proposed in the past decades, there still lacks a thorough comparison to cover all the state-of-the-art algorithms and evaluate their quality using datasets in diversified motion patterns. Hence, it still remains a challenge for GPS data collectors to determine a proper algorithm in a concrete application. In addition, almost the entire line of previous methods uses error-based metrics to evaluate the compression quality, while ignoring their usability in supporting spatio-temporal queries on top of the reduced database. To bridge these gaps, we conduct so far the most comprehensive evaluation on trajectory simplification techniques. We compare the performance of 25 algorithms in total using five real datasets in different motion patterns. According to the experimental findings, we present useful guidance for the selection or development of effective trajectory simplification algorithms.

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