Discovering Intraday Market Risk Exposures in Unstructured Data Sources: The Case of Corporate Disclosures

This chapter describes the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system to jointly store, manage, retrieve, and query interlinked structured and unstructured data, especially designed to manage all the data involved in Knowledge Extraction applications. The chapter presents the concept, design, function and implementation of the KnowledgeStore, and reports on its concrete usage in four application scenarios within the NewsReader EU project, where it has been successfully used to store and support the querying of millions of news articles interlinked with billions of RDF triples, both extracted from text and imported from Linked Open Data sources.

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Corporate Disclosures of Mid-Caps and Market Risk Indicators

Corporate Disclosures and Financial Risk Assessment ◽

10.1007/978-3-658-12460-1_4 ◽

2016 ◽

pp. 41-76

Author(s):

Philipp Kissing

Keyword(s):

Market Risk ◽

Risk Indicators ◽

Corporate Disclosures

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A Scalable Business Intelligence Decision-Making System in the Era of Big Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3251.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2034-2042

Keyword(s):

Big Data ◽

Data Warehouse ◽

Data Storage ◽

Business Intelligence ◽

Unstructured Data ◽

Data Sources ◽

Second Step ◽

Decision Making System ◽

Set Up

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

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Exploring the Link Between Economic Complexity and Emergent Economic Activities

National Institute Economic Review ◽

10.1177/002795011924900114 ◽

2019 ◽

Vol 249 ◽

pp. R47-R58 ◽

Cited By ~ 2

Author(s):

Alex Bishop ◽

Juan Mateos-Garcia

Keyword(s):

Economic Development ◽

Economic Performance ◽

Potential Benefit ◽

Unstructured Data ◽

Data Sources ◽

Economic Activities ◽

Local Economies ◽

Economic Complexity ◽

Two Indices ◽

Official Data

Recent studies have shown a strong link between the complexity of economies and their economic development. There remain gaps in our understanding of the mechanisms underpinning these links, in part because they are difficult to analyse with highly aggregated, official data sources that do not capture the emergence of new industrial activities, a potential benefit from complexity. We seek to address some of these gaps by calculating two indices of economic complexity for functional local economies (Travel to Work Areas) in Great Britain, and explore their link with these locations’ economic performance. Seeking to gain a better understanding of the mechanism connecting economic complexity with economic performance, we create a measure of emergent technological activity in a location based on a combination of novel data sources including text from UK business websites and CrunchBase, a technology company directory, and study its link with economic complexity. Our results highlight the potential value of novel, unstructured data sources for the analysis of the links between economic complexity and regional economic development.

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Corporate governance in relationship with bank risk management

International Journal of Financial Engineering ◽

10.1142/s2424786321500341 ◽

2021 ◽

pp. 2150034

Author(s):

Ika Permatasari

Keyword(s):

Risk Management ◽

Corporate Governance ◽

Credit Risk ◽

Liquidity Risk ◽

Market Risk ◽

Data Sources ◽

Annual Reports ◽

Bank Risk ◽

Financial Reports ◽

The Relationship

The purpose of this research is to examine the relationship between corporate governance and risk management of Indonesian banks. Bank risk managements are measured by market risk, credit risk, and liquidity risk. The samples used in this study were all banks registered in Indonesia during the 2010–2016 period. The data sources were obtained from the annual reports and bank financial reports. The results show that corporate governance implementation in Indonesia was able to affect credit risk and liquidity risk. There were differences in credit risk and liquidity risk in banks with different governance ratings, but not at market risk.

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Updating the paradigm of official statistics: New quality criteria for integrating new data and methods in official statistics

Statistical Journal of the IAOS ◽

10.3233/sji-200711 ◽

2020 ◽

pp. 1-18

Author(s):

Sofie De Broe ◽

Peter Struijs ◽

Piet Daas ◽

Arnout van Delden ◽

Joep Burger ◽

...

Keyword(s):

Paradigm Shift ◽

Data Science ◽

Quality Criteria ◽

Unstructured Data ◽

Data Sources ◽

Cultural Barriers ◽

Official Statistics ◽

Knowledge And Skills ◽

Production Processes

This paper aims to elicit a discussion of the existence of a paradigm shift in official statistics through the emergence of new (unstructured) data sources and methods that may not adhere to established and existing statistical practices and quality frameworks. The paper discusses strengths and weaknesses of several data sources. Furthermore, it discusses methodological, technical and cultural barriers in dealing with new data and methods in data science; cultural as in the culture that reigns in an area of expertise or approach. The paper concludes with suggestions of updating the existing quality frameworks. We take the position that there is no paradigm shift but that the existing production processes should be adjusted and that existing quality frameworks should be updated in order for official statistics to benefit from the fusion of data, knowledge and skills among survey methodologists and data scientists.

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Data Ingestion using a Novel Method: H-Stream Framework

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6045.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1945-1949

Keyword(s):

Real Time ◽

Stream Processing ◽

Batch Processing ◽

Multimedia Data ◽

Unstructured Data ◽

Data Sources ◽

Apache Hadoop ◽

Novel Method ◽

Frame Work ◽

Data Ingestion

Current huge volumes of data is generated from wide variety of data sources and there is lot of demand for processing, this data. Apache Hadoop is designed for batch processing. Though Hadoop is used for batch processing there is lot of requirement for real time stream processing and querying on unstructured data. Data ingestion tools of Hadoop are playing a key role in processing of streamed log data. With the increase of volume of the data performance of data ingestion tools goes down linearly. In this paper we discuss solutions for performance issues of data ingestion tools, capturing and processing of streamed multimedia data along with real-time stream processing with the help of frame work known as H-Stream framework. (Abstract)

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