A Data Analytics Architecture for the Exploratory Analysis of High-Frequency Market Data
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| Title: | A Data Analytics Architecture for the Exploratory Analysis of High-Frequency Market Data |
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| Authors: | Ng, SL, Rabhi, F |
| Source: | Lecture Notes in Business Information Processing ISBN: 9783031316708 |
| Publisher Information: | Springer International Publishing, 2023. |
| Publication Year: | 2023 |
| Subject Terms: | 4605 Data Management and Data Science, 46 Information and Computing Sciences, Networking and Information Technology R&D (NITRD), anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, Bioengineering |
| Description: | The development of cloud computing and database systems has increased the availability of high-frequency market data. An increasing number of researchers and domain experts are interested in analyzing such datasets in an ad-hoc manner. In spite of this, high-frequency data analysis requires a combination of domain knowledge and IT skills due to the need for data standardisation and extensive usage of computational resources. This paper proposes an architecture design for integrating data acquisition, analytics services, and visualisation to reduce the technical challenges for researchers and experts to analyze high-frequency market data. A case study demonstrates how the design can assist experts to invoke different analytics services within a consistent operational environment backed by analytics tools and resources such as a GCP’s Big Query running over a Refinitiv Tick History database and a Jupyter notebook. |
| Document Type: | Part of book or chapter of book |
| File Description: | application/pdf |
| Language: | English |
| DOI: | 10.1007/978-3-031-31671-5_1 |
| Rights: | Springer Nature TDM CC BY |
| Accession Number: | edsair.doi.dedup.....2ffb0eef19b28a502a9ca11a9d05b65e |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The development of cloud computing and database systems has increased the availability of high-frequency market data. An increasing number of researchers and domain experts are interested in analyzing such datasets in an ad-hoc manner. In spite of this, high-frequency data analysis requires a combination of domain knowledge and IT skills due to the need for data standardisation and extensive usage of computational resources. This paper proposes an architecture design for integrating data acquisition, analytics services, and visualisation to reduce the technical challenges for researchers and experts to analyze high-frequency market data. A case study demonstrates how the design can assist experts to invoke different analytics services within a consistent operational environment backed by analytics tools and resources such as a GCP’s Big Query running over a Refinitiv Tick History database and a Jupyter notebook. |
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| DOI: | 10.1007/978-3-031-31671-5_1 |