Extracting Insights From Bitcoin Transactions

2021 ◽  
pp. 45-65
Author(s):  
Rim Moussa ◽  
Alfredo Cuzzocrea
Keyword(s):  
Data Warehouse ◽  
Data Models ◽  
Record Keeping ◽  
Distributed Ledger ◽  
Atomic Transactions

Bitcoin is the most well-known cryptocurrency. It was first released in 2009 by Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in the blockchain. The latter is a public immutable distributed ledger that operates without the need of a trusted record keeping authority or a central intermediary. It provides OLTP capabilities with both atomic transactions and data durability guarantees for blockchain transactions. Blockchain ledgers were not designed to perform analytics questions. The availability of the entire bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analyzing the transactions to obtain insights on users/entities patterns and transactions patterns. For these purposes, the authors need to store and analyze cryptocurrency transactions in a data warehouse. In this chapter, they investigate public blockchain datasets, and they overview different data models for setting up a data warehouse appliance of cryptocurrencies.

Universal Data Warehousing Based on a Meta-Data Modeling Approach

2003 ◽  
Vol 12 (03) ◽  
pp. 325-363 ◽  
Author(s):  
Joseph Fong ◽  
Qing Li ◽  
Shi-Ming Huang
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Object Oriented ◽  
Data Models ◽  
Heterogeneous Databases ◽  
Materialized Views ◽  
Prototype System ◽  
Relational View ◽  
Metadata Model ◽  
Star Schema

Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.

A comparative analysis of data warehouse data models

2016 ◽  
Author(s):  
Ivan Bojicic ◽  
Zoran Marjanovic ◽  
Nina Turajlic ◽  
Marko Petrovic ◽  
Milica Vuckovic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Warehouse ◽  
Data Models

scholarly journals ABOUT DATA MODELING SUBJECT DOMAINS PRACTICE-ORIENTED DIRECTION FOR UNIVERSAL SYSTEM OF STORAGE AND PROCESSING DATA

2017 ◽  
pp. 4-12 ◽  
Author(s):  
L. V. Rudikova ◽  
E. V. Zhavnerko
Keyword(s):  
Data Warehouse ◽  
Data Storage ◽  
Data Modeling ◽  
Analysis Data ◽  
Data Models ◽  
Universal System ◽  
Current Time ◽  
High Data ◽  
General Data Model ◽  
Subject Domains

This article describes data modeling for practice-oriented subject domains they are basis of general data model for data warehouse creation. Describes short subject domains characteristic relationship to different types of any human activities at the current time. Offered appropriate data models, considered relationship between them as data processing and data warehouse creation, which can be built on information data storage technology and which has some characteristics as extensible complex subject domain, data integration, which get from any data sources, data time invariance with required temporal marks, relatively high data stability, search necessary compromises in data redundancy, system blocks modularity, flexibility and extensibility of architecture, high requirements to data storage security. It’s proposed general approach of data collection and data storage, appropriate data models, in the future, will integrate in one database scheme and create generalized scheme of data warehouse as type «constellation of facts». For getting of data models applies structural methodology and consider general principles of conceptual design. Using complex system, which can work with some information sources and represent data in convenient view for users will in-demand for analysis data selected subject domains and determination of possible relationships.

UML-Based Data Warehouse Design Using Temporal Dimensional Modelling

2020 ◽  
Vol 12 (3) ◽  
pp. 1-19
Author(s):  
G. Sekhar Reddy ◽  
Chittineni Suneetha
Keyword(s):  
Data Warehouse ◽  
University Education ◽  
Temporal Dimension ◽  
Record Keeping ◽  
Business Goals ◽  
Temporal Variables ◽  
History Of ◽  
Temporal Dimensions ◽  
Multidimensional Modelling

The design of a data warehouse system deals with tasks such as data source administration, ETL processing, multidimensional modelling, data mart specification, and end-user tool development. In the last decade, numerous techniques have been presented to cover all the aspects of DW. However, none of these techniques stated the recent necessities of DW like visualization, temporal dimensions, record keeping, and so on. To overcome these issues, this paper proposes a UML based DW with temporal dimensions. This framework designs time-dependent DW that allows end-users to store history of variations for long term. Besides, it authorizes to visualize the business goals of organizations in the form of attribute tree via UML, which is designed after receiving user necessities and later reconciling with temporal variables. The implementation of proposed technique is detailed with university education database for quality improvement. The proposed technique is found to be useful in terms of temporal dimension, long-term record keeping, and easy to make decision goals through attribute trees.

Temporal Semistructured Data Models and Data Warehouses

2011 ◽  
pp. 277-297 ◽  
Author(s):  
Carlo Combi ◽  
Barbara Oliboni
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Heterogeneous Data ◽  
Data Models ◽  
Semistructured Data ◽  
Data Sources ◽  
Time Varying ◽  
Data Warehouses ◽  
Time Dimension ◽  
Heterogeneous Data Sources

This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model. We consider issues related to the representation of semistructured data warehouses, and discuss the set of constraints needed to manage in a correct way the warehouse time, i.e. the time dimension considered storing data in the data warehouse itself. We use a temporal semistructured data model because a data warehouse can contain data coming from different and heterogeneous data sources. This means that data stored in a data warehouse are semistructured in nature, i.e. in different documents the same information can be represented in different ways, and moreover, the document schemata can be available or not. Moreover, information stored into a data warehouse is often time varying, thus as for semistructured data, also in the data warehouse context, it could be useful to consider time.

Record Keeping Guidelines.

2007 ◽  
Vol 62 (9) ◽  
pp. 993-1004 ◽  
Author(s):  
Keyword(s):  
Record Keeping
Sign in / Sign up

Export Citation Format

Share Document