Bi-temporal Timeline Index: A data structure for Processing Queries on bi-temporal data

Kaufmann, Martin; Fischer, Peter M.; May, Norman; Ge, Chang; Goel, Anil Kumar; Kossmann, Donald

doi:10.1109/icde.2015.7113307

Cited by 41 publications

(67 citation statements)

References 19 publications

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“…Descriptions of "Big Data" systems in production environments typically mention data sizes in the hundreds of TB to hundreds of PB [27,4,17] or trillions to hundreds of trillions of rows [12]. "Big Data" research studies on the other hand tend to work with much smaller datasets, ranging from hundreds of GB [6,18,16,24] to a few TBs [29,28,23,1]. The largest dataset we have seen used in an existing "Big Data" study is 16 TB [9].…”

Section: Related Workmentioning

confidence: 98%

IoTAbench

Arlitt

Marwah

Bellala

et al. 2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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The commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This "Internetof-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer "Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform "apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster.

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Section: Related Workmentioning

confidence: 98%

IoTAbench

Arlitt

Marwah

Bellala

et al. 2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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“…HANA supports temporal queries, such as temporal aggregation, time travel and temporal join, based on a unified index structure called the Timeline Index [88], [242], [247]. For every logical table, HANA keeps the current version of the table in a Current Table and the whole history of previous versions in a Temporal Table, accompanied with a Timeline Index to facilitate temporal queries.…”

Section: Sap Hanamentioning

confidence: 99%

“…It provides rich data analytics functionality by offering multiple query language interfaces (e.g., standard SQL, SQLScript, MDX, WIPE, FOX and R), which makes it easy to push down more application semantics into the data management layer, thus avoiding heavy data transfer cost. It supports temporal queries based on the Timeline Index [242] naturally as data is versioned in HANA. It provides snapshot isolation based on multi-version concurrency control, transaction semantics based on optimized two-phase commit protocol (2PC) [243], and fault-tolerance by logging and periodic checkpointing into GPFS file system [148].…”

Section: Sap Hanamentioning

confidence: 99%

In-Memory Big Data Management and Processing: A Survey

Zhang

Chen

Ooi

et al. 2015

IEEE Trans. Knowl. Data Eng.

362

156

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Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

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“…Given the structural similarity and the wide recognition, TPC-C has been used for this purpose, e.g., in [2]. We also used a similar approach (with additional timestamp assignment) in a previous version of the benchmark [8], but this proved to not be fully adequate: The set of update scenarios is quite small, and does not provide much emphasis on temporal aspects such as timestamp correlations. The query mix also constrains the flexibility in terms of temporal properties, e.g., since a fixed ratio of updates needs to go to specific tables.…”

Section: Benchmark Datamentioning

confidence: 99%

TPC-BiH: A Benchmark for Bitemporal Databases

Kaufmann

Fischer

May

et al. 2014

Performance Characterization and Benchmarking

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Abstract. An increasing number of applications such as risk evaluation in banking or inventory management require support for temporal data. After more than a decade of standstill, the recent adoption of some bitemporal features in SQL:2011 has reinvigorated the support among commercial database vendors, who incorporate an increasing number of relevant bitemporal features. Naturally, assessing the performance and scalability of temporal data storage and operations is of great concern for potential users. The cost of keeping and querying history with novel operations (such as time travel, temporal joins or temporal aggregations) is not adequately reflected in any existing benchmark. In this paper, we present a benchmark proposal which provides comprehensive coverage of the bitemporal data management. It builds on the solid foundations of TPC-H but extends it with a rich set of queries and update scenarios. This workload stems both from real-life temporal applications from SAP's customer base and a systematic coverage of temporal operators proposed in the academic literature. We present preliminary results of our benchmark on a number of temporal database systems, also highlighting the need for certain language extensions.

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Bi-temporal Timeline Index: A data structure for Processing Queries on bi-temporal data

Cited by 41 publications

References 19 publications

IoTAbench

IoTAbench

In-Memory Big Data Management and Processing: A Survey

TPC-BiH: A Benchmark for Bitemporal Databases

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