This document provides an overview of key concepts related to decision support systems (DSS) and data warehousing. It defines DSS as interactive computer systems that help decision makers use data, documents, models and communication technologies to identify and solve problems. It then discusses operational databases and how they differ from data warehouses in areas like data type, focus, users and more. Finally, it defines key characteristics of a data warehouse as being subject-oriented, integrated, time-variant and non-volatile to support management decision making.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Bandwidth utilization techniques like multiplexing and spreading can help efficiently use available bandwidth. Multiplexing allows simultaneous transmission of multiple signals over a single data link by techniques like frequency division multiplexing (FDM), wavelength division multiplexing (WDM), and time division multiplexing (TDM). FDM divides the link into frequency channels. WDM is similar but uses light signals transmitted through fiber. TDM divides the link into timed slots and allows digital signals to share the bandwidth. Efficiency can be improved through techniques like multilevel multiplexing, multiple slot allocation, and pulse stuffing to handle disparities in data rates.
Developed by ITU-T, ISDN is a set of protocols that combines digital telephony and data transport services to digitise the telephone network to permit the transmission of audio, video and text over existing telephone line. ISDN is an effort to standardise subscriber services, provide user or network interface and facilitate the inter-networking capabilities of existing voice and data networks. The goal of ISDN is to form a wide area network that provides universal end-to-end connectivity over digital media by integrating separate transmission services into one without adding new links or subscriber links.
Interrupt is a process that holds the microprocessor for a while and decide what will be next job that will done by the microprocesssor. Interrupt types ,SIM ,RIM ,DMA ,Maskable interrupt ,Non-Maskable interrupt,Trap,RST and many more has been discussed on this powerpoint . You will be able to know each of the interrupt and their functions from the slides ,some diagram that will help you to develop your knowledge about interrupt . Hardware interrupts are used by devices to communicate that they require attention from the operating system.Hardware interrupts are used by devices to communicate that they require attention from the operating system.Internally, hardware interrupts are implemented using electronic alerting signals that are sent to the processor from an external device, which is either a part of the computer itself, such as a disk controller, or an external peripheral. For example, pressing a key on the keyboard or moving the mouse triggers hardware interrupts that cause the processor to read the keystroke or mouse position.software interrupt is caused either by an exceptional condition in the processor itself, or a special instruction in the instruction set which causes an interrupt when it is executed. The former is often called a trap or exception and is used for errors or events occurring during program execution that are exceptional enough that they cannot be handled within the program itself. For example, a divide-by-zero exception will be thrown if the processor's arithmetic logic unit is commanded to divide a number by zero as this instruction is an error and impossible. The operating system will catch this exception, and can choose to abort the instruction. Software interrupt instructions can function similarly to subroutine calls and are used for a variety of purposes, such as to request services from device drivers, like interrupts sent to and from a disk controller to request reading or writing of data to and from the disk.
Register transfer language is used to describe micro-operation transfers between registers. It represents the sequence of micro-operations performed on binary information stored in registers and the control that initiates the sequences. A register is a group of flip-flops that store binary information. Information can be transferred between registers using replacement operators and control functions. Common bus systems using multiplexers or three-state buffers allow efficient information transfer between multiple registers by selecting one register at a time to connect to the shared bus lines. Memory transfers are represented by specifying the memory word selected by the address in a register and the data register involved in the transfer.
Buses transfer data and communication signals within a computer. They allow different components like the CPU, memory, and input/output devices to exchange information. The bus width and clock speed determine how much data can be transferred at once and how quickly. Wider buses and faster clock speeds improve performance by allowing more data to be processed in less time. A computer has several types of buses that connect different internal components like the processor, cache, and expansion ports.
The document provides information about input/output management in operating systems. It discusses I/O devices, device controllers, direct memory access and DMA controllers. Some key points include:
I/O devices are divided into block devices which access fixed size blocks of data and character devices which access data as a sequential stream. Device controllers act as an interface between devices and device drivers. Direct memory access allows data transfer between memory and devices without CPU involvement by using a DMA controller. DMA controllers program data transfers and arbitrate bus access.
This document discusses different types of data transfer modes between I/O devices and memory, including programmed I/O, interrupt-driven I/O, and direct memory access (DMA). It explains that DMA allows I/O devices to access memory directly without CPU intervention by using a DMA controller. The basic operations of DMA include the DMA controller gaining control of the system bus, transferring data directly between memory and I/O devices by updating address and count registers, and then relinquishing control back to the CPU. Different DMA transfer techniques like byte stealing, burst, and continuous modes are also covered.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Bandwidth utilization techniques like multiplexing and spreading can help efficiently use available bandwidth. Multiplexing allows simultaneous transmission of multiple signals over a single data link by techniques like frequency division multiplexing (FDM), wavelength division multiplexing (WDM), and time division multiplexing (TDM). FDM divides the link into frequency channels. WDM is similar but uses light signals transmitted through fiber. TDM divides the link into timed slots and allows digital signals to share the bandwidth. Efficiency can be improved through techniques like multilevel multiplexing, multiple slot allocation, and pulse stuffing to handle disparities in data rates.
Developed by ITU-T, ISDN is a set of protocols that combines digital telephony and data transport services to digitise the telephone network to permit the transmission of audio, video and text over existing telephone line. ISDN is an effort to standardise subscriber services, provide user or network interface and facilitate the inter-networking capabilities of existing voice and data networks. The goal of ISDN is to form a wide area network that provides universal end-to-end connectivity over digital media by integrating separate transmission services into one without adding new links or subscriber links.
Interrupt is a process that holds the microprocessor for a while and decide what will be next job that will done by the microprocesssor. Interrupt types ,SIM ,RIM ,DMA ,Maskable interrupt ,Non-Maskable interrupt,Trap,RST and many more has been discussed on this powerpoint . You will be able to know each of the interrupt and their functions from the slides ,some diagram that will help you to develop your knowledge about interrupt . Hardware interrupts are used by devices to communicate that they require attention from the operating system.Hardware interrupts are used by devices to communicate that they require attention from the operating system.Internally, hardware interrupts are implemented using electronic alerting signals that are sent to the processor from an external device, which is either a part of the computer itself, such as a disk controller, or an external peripheral. For example, pressing a key on the keyboard or moving the mouse triggers hardware interrupts that cause the processor to read the keystroke or mouse position.software interrupt is caused either by an exceptional condition in the processor itself, or a special instruction in the instruction set which causes an interrupt when it is executed. The former is often called a trap or exception and is used for errors or events occurring during program execution that are exceptional enough that they cannot be handled within the program itself. For example, a divide-by-zero exception will be thrown if the processor's arithmetic logic unit is commanded to divide a number by zero as this instruction is an error and impossible. The operating system will catch this exception, and can choose to abort the instruction. Software interrupt instructions can function similarly to subroutine calls and are used for a variety of purposes, such as to request services from device drivers, like interrupts sent to and from a disk controller to request reading or writing of data to and from the disk.
Register transfer language is used to describe micro-operation transfers between registers. It represents the sequence of micro-operations performed on binary information stored in registers and the control that initiates the sequences. A register is a group of flip-flops that store binary information. Information can be transferred between registers using replacement operators and control functions. Common bus systems using multiplexers or three-state buffers allow efficient information transfer between multiple registers by selecting one register at a time to connect to the shared bus lines. Memory transfers are represented by specifying the memory word selected by the address in a register and the data register involved in the transfer.
Buses transfer data and communication signals within a computer. They allow different components like the CPU, memory, and input/output devices to exchange information. The bus width and clock speed determine how much data can be transferred at once and how quickly. Wider buses and faster clock speeds improve performance by allowing more data to be processed in less time. A computer has several types of buses that connect different internal components like the processor, cache, and expansion ports.
The document provides information about input/output management in operating systems. It discusses I/O devices, device controllers, direct memory access and DMA controllers. Some key points include:
I/O devices are divided into block devices which access fixed size blocks of data and character devices which access data as a sequential stream. Device controllers act as an interface between devices and device drivers. Direct memory access allows data transfer between memory and devices without CPU involvement by using a DMA controller. DMA controllers program data transfers and arbitrate bus access.
This document discusses different types of data transfer modes between I/O devices and memory, including programmed I/O, interrupt-driven I/O, and direct memory access (DMA). It explains that DMA allows I/O devices to access memory directly without CPU intervention by using a DMA controller. The basic operations of DMA include the DMA controller gaining control of the system bus, transferring data directly between memory and I/O devices by updating address and count registers, and then relinquishing control back to the CPU. Different DMA transfer techniques like byte stealing, burst, and continuous modes are also covered.
Data communication : entails electronically exchanging data or information. It is the movement of computer information from one point to another by means of electrical or optical transmission system. This system often is called data communication networks.
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
DISTRIBUTED DATABASE WITH RECOVERY TECHNIQUESAAKANKSHA JAIN
Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
Distributed database design refers to the following problem:
Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
There are two issues:
(i) Data fragmentation which determines how the data should be fragmented.
(ii) Data allocation which determines how the fragments should be allocated.
Ethernet is a family of networking technologies commonly used in LANs, MANs and WANs. It was first standardized in 1983 at 10 Mbps and has since been updated to support higher speeds up to 10 Gbps. Fast Ethernet runs at 100 Mbps using the same frame format as standard Ethernet. Gigabit Ethernet runs at 1 Gbps while maintaining compatibility. Ten-Gigabit Ethernet operates at 10 Gbps while keeping the same frame format as prior standards.
This document discusses stack organization and operations. A stack is a last-in, first-out data structure where items added last are retrieved first. It uses a stack pointer to track the top of the stack. Common operations are push, which adds an item to the top of the stack, and pop, which removes an item from the top. Stacks can be implemented with registers, using a stack pointer and data register. Reverse Polish notation places operators after operands, making it suitable for stack-based expression evaluation.
PCI is a widely used interface standard developed in 1993 to connect processors to chipsets. It provides faster data transfer speeds than the earlier ISA standard. Features include synchronous bus architecture, 64-bit addressing, and burst mode data transfer.
USB is a universal serial bus standard created in 1996 to connect peripherals to computers. Up to 127 devices can connect to a single USB host controller via cables up to 5 meters long without hubs or 40 meters with hubs. USB allows for plug-and-play connectivity of devices such as mice, keyboards, cameras, and storage.
SCSI is an interface standard developed in 1981 for connecting computers and peripheral devices via daisy-chained ports. Up to 8 or 16
Digital Data to Digital Signal ConversionArafat Hossan
Digital to Digital Conversion
Conversion Techniques
Line Coding
Relationship Between Data Rate and Signal Rate
Line Coding Schemes
Unipolar
Polar
Bipolar
Block Coding
Scrambling
Clipping identifies portions of a scene outside a specified clip window region. There are different types of clipping for different graphics elements. The Cohen-Sutherland algorithm assigns a binary code to line endpoints based on their position relative to the clip window boundaries, and uses logical AND operations on the codes to determine if a line needs clipping or can be fully accepted or rejected. It iteratively clips portions of a line outside the window until the line is fully processed.
Discussed different types of dynamic interconnection networks. Graphically demonstrated single and multiple bus interconnection networks. Discussed different types of switch based interconnection networks. Graphically shown the mechanisms of crossbar, single and multistage interconnection networks. Graphically explained the working principle of omega network, Benes network, and baseline networks.
Modem is a network device that enables a computer to transfer data from telephone lines to computers and computers to telephone lines.
The word modem is derived from modulator and demodulator.
Modem performs modulation and demodulation.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
The main Objective of this presentation is to define computer buses , especially system bus . which is consists of data bus , address bus and control bus.
Interconnection Network
in this presentation there are some explain to Interconnection Network , and espically in computer architecture and parallel processing.
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
The document discusses different memory management strategies:
- Swapping allows processes to be swapped temporarily out of memory to disk, then back into memory for continued execution. This improves memory utilization but incurs long swap times.
- Contiguous memory allocation allocates processes into contiguous regions of physical memory using techniques like memory mapping and dynamic storage allocation with first-fit or best-fit. This can cause external and internal fragmentation over time.
- Paging permits the physical memory used by a process to be noncontiguous by dividing memory into pages and mapping virtual addresses to physical frames, allowing more efficient use of memory but requiring page tables for translation.
Concurrency Control in Distributed Database.Meghaj Mallick
The document discusses various techniques for concurrency control in distributed databases, including locking-based protocols and timestamp-based protocols. Locking-based protocols use exclusive and shared locks to control concurrent access to data items. They can be implemented using a single or distributed lock manager. Timestamp-based protocols assign each transaction a unique timestamp to determine serialization order and manage concurrent execution.
The document discusses different line and area attributes that can be used to display graphics primitives. It describes parameters like line type (solid, dashed, dotted), width, color, and fill style (solid, patterned, hollow). It explains how these attributes can be set using functions like setLineType() and setInteriorStyle(). Pixel masks and adjusting pixel counts are used to properly render dashed lines at different angles. Color can be represented directly or indirectly via color codes mapped to an output device's color capabilities. Patterns for filled areas are defined via 2D color arrays.
This document discusses concurrency control algorithms for distributed database systems. It describes distributed two-phase locking (2PL), wound-wait, basic timestamp ordering, and distributed optimistic concurrency control algorithms. For distributed 2PL, transactions lock data items in a growing phase and release locks in a shrinking phase. Wound-wait prevents deadlocks by aborting younger transactions that wait on older ones. Basic timestamp ordering orders transactions based on their timestamps to ensure serializability. The distributed optimistic approach allows transactions to read and write freely until commit, when certification checks for conflicts. Maintaining consistency across distributed copies is important for concurrency control algorithms.
The document provides an introduction to the concept of data mining. It discusses the evolution of data analysis techniques from empirical to computational to data-driven approaches. Data mining is presented as a natural evolution to analyze massive data sets and discover useful patterns. Key aspects of data mining covered include its functionality, types of data and knowledge that can be mined, major issues, and its relationship to other fields such as machine learning, statistics, and databases.
The document provides an overview of data warehousing concepts including:
- William Inmon is considered the "father of data warehousing" and has written extensively on the topic.
- A data warehouse is a collection of integrated subject-oriented databases designed to support decision-making. It contains non-volatile, time-variant data from one or more sources.
- An operational data store feeds the data warehouse with a stream of raw data. A data mart offers targeted access to a subset of warehouse data. Metadata provides data about the structure and meaning of warehouse data.
Data communication : entails electronically exchanging data or information. It is the movement of computer information from one point to another by means of electrical or optical transmission system. This system often is called data communication networks.
This document provides an overview of the Turing machine. It describes the Turing machine as an abstract computational model invented by Alan Turing in 1936. A Turing machine consists of an infinite tape divided into cells, a tape head that reads and writes symbols on the tape, and a state table that governs the machine's behavior. The document then explains the formal definition of a Turing machine, provides an example of how it works, discusses properties like decidability and recognizability, and covers modifications like multi-tape and non-deterministic Turing machines. It concludes by discussing the halting problem and explaining how Turing machines demonstrate the power and applications of computational theory.
DISTRIBUTED DATABASE WITH RECOVERY TECHNIQUESAAKANKSHA JAIN
Distributed Database Designs are nothing but multiple, logically related Database systems, physically distributed over several sites, using a Computer Network, which is usually under a centralized site control.
Distributed database design refers to the following problem:
Given a database and its workload, how should the database be split and allocated to sites so as to optimize certain objective function
There are two issues:
(i) Data fragmentation which determines how the data should be fragmented.
(ii) Data allocation which determines how the fragments should be allocated.
Ethernet is a family of networking technologies commonly used in LANs, MANs and WANs. It was first standardized in 1983 at 10 Mbps and has since been updated to support higher speeds up to 10 Gbps. Fast Ethernet runs at 100 Mbps using the same frame format as standard Ethernet. Gigabit Ethernet runs at 1 Gbps while maintaining compatibility. Ten-Gigabit Ethernet operates at 10 Gbps while keeping the same frame format as prior standards.
This document discusses stack organization and operations. A stack is a last-in, first-out data structure where items added last are retrieved first. It uses a stack pointer to track the top of the stack. Common operations are push, which adds an item to the top of the stack, and pop, which removes an item from the top. Stacks can be implemented with registers, using a stack pointer and data register. Reverse Polish notation places operators after operands, making it suitable for stack-based expression evaluation.
PCI is a widely used interface standard developed in 1993 to connect processors to chipsets. It provides faster data transfer speeds than the earlier ISA standard. Features include synchronous bus architecture, 64-bit addressing, and burst mode data transfer.
USB is a universal serial bus standard created in 1996 to connect peripherals to computers. Up to 127 devices can connect to a single USB host controller via cables up to 5 meters long without hubs or 40 meters with hubs. USB allows for plug-and-play connectivity of devices such as mice, keyboards, cameras, and storage.
SCSI is an interface standard developed in 1981 for connecting computers and peripheral devices via daisy-chained ports. Up to 8 or 16
Digital Data to Digital Signal ConversionArafat Hossan
Digital to Digital Conversion
Conversion Techniques
Line Coding
Relationship Between Data Rate and Signal Rate
Line Coding Schemes
Unipolar
Polar
Bipolar
Block Coding
Scrambling
Clipping identifies portions of a scene outside a specified clip window region. There are different types of clipping for different graphics elements. The Cohen-Sutherland algorithm assigns a binary code to line endpoints based on their position relative to the clip window boundaries, and uses logical AND operations on the codes to determine if a line needs clipping or can be fully accepted or rejected. It iteratively clips portions of a line outside the window until the line is fully processed.
Discussed different types of dynamic interconnection networks. Graphically demonstrated single and multiple bus interconnection networks. Discussed different types of switch based interconnection networks. Graphically shown the mechanisms of crossbar, single and multistage interconnection networks. Graphically explained the working principle of omega network, Benes network, and baseline networks.
Modem is a network device that enables a computer to transfer data from telephone lines to computers and computers to telephone lines.
The word modem is derived from modulator and demodulator.
Modem performs modulation and demodulation.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
The main Objective of this presentation is to define computer buses , especially system bus . which is consists of data bus , address bus and control bus.
Interconnection Network
in this presentation there are some explain to Interconnection Network , and espically in computer architecture and parallel processing.
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
The document discusses different memory management strategies:
- Swapping allows processes to be swapped temporarily out of memory to disk, then back into memory for continued execution. This improves memory utilization but incurs long swap times.
- Contiguous memory allocation allocates processes into contiguous regions of physical memory using techniques like memory mapping and dynamic storage allocation with first-fit or best-fit. This can cause external and internal fragmentation over time.
- Paging permits the physical memory used by a process to be noncontiguous by dividing memory into pages and mapping virtual addresses to physical frames, allowing more efficient use of memory but requiring page tables for translation.
Concurrency Control in Distributed Database.Meghaj Mallick
The document discusses various techniques for concurrency control in distributed databases, including locking-based protocols and timestamp-based protocols. Locking-based protocols use exclusive and shared locks to control concurrent access to data items. They can be implemented using a single or distributed lock manager. Timestamp-based protocols assign each transaction a unique timestamp to determine serialization order and manage concurrent execution.
The document discusses different line and area attributes that can be used to display graphics primitives. It describes parameters like line type (solid, dashed, dotted), width, color, and fill style (solid, patterned, hollow). It explains how these attributes can be set using functions like setLineType() and setInteriorStyle(). Pixel masks and adjusting pixel counts are used to properly render dashed lines at different angles. Color can be represented directly or indirectly via color codes mapped to an output device's color capabilities. Patterns for filled areas are defined via 2D color arrays.
This document discusses concurrency control algorithms for distributed database systems. It describes distributed two-phase locking (2PL), wound-wait, basic timestamp ordering, and distributed optimistic concurrency control algorithms. For distributed 2PL, transactions lock data items in a growing phase and release locks in a shrinking phase. Wound-wait prevents deadlocks by aborting younger transactions that wait on older ones. Basic timestamp ordering orders transactions based on their timestamps to ensure serializability. The distributed optimistic approach allows transactions to read and write freely until commit, when certification checks for conflicts. Maintaining consistency across distributed copies is important for concurrency control algorithms.
The document provides an introduction to the concept of data mining. It discusses the evolution of data analysis techniques from empirical to computational to data-driven approaches. Data mining is presented as a natural evolution to analyze massive data sets and discover useful patterns. Key aspects of data mining covered include its functionality, types of data and knowledge that can be mined, major issues, and its relationship to other fields such as machine learning, statistics, and databases.
The document provides an overview of data warehousing concepts including:
- William Inmon is considered the "father of data warehousing" and has written extensively on the topic.
- A data warehouse is a collection of integrated subject-oriented databases designed to support decision-making. It contains non-volatile, time-variant data from one or more sources.
- An operational data store feeds the data warehouse with a stream of raw data. A data mart offers targeted access to a subset of warehouse data. Metadata provides data about the structure and meaning of warehouse data.
This document provides an introduction and overview of the DBM630: Data Mining and Data Warehousing course. It outlines the course syllabus, textbooks, assessment tasks, schedule, prerequisites, and provides a high-level introduction to data mining and data warehousing concepts including definitions, processes, applications and evolution of database technologies.
The document discusses several case studies and applications of data mining including:
1) Customer attrition prediction helped a mobile phone company reduce attrition rates from over 2%/month to under 1.5%/month.
2) Credit risk models used by banks to predict loan defaults enabled proliferation of mortgages and credit cards.
3) Amazon's product recommendations were successful by clustering customers based on products purchased.
4) A case study of MetLife found $30 million in fraudulent insurance claims through data mining of a $50 million consolidated database within companies worldwide to detect fraud like rate evasion faster than manual methods.
This document discusses various classification and prediction techniques including Naive Bayes classification, regression, and support vector machines (SVM). It covers topics such as Naive Bayes assumptions, dealing with missing data, numeric attributes, and Bayesian belief networks. Statistical modeling approaches like Naive Bayes make independence assumptions between attributes. Regression can be used for numerical prediction problems.
This document discusses data mining concepts including data preprocessing and postprocessing. It covers the differences between data mining, machine learning, and statistics. Data mining aims to discover knowledge from data in an automatic or semi-automatic way. Both data mining and machine learning use techniques to generalize from data, but data mining focuses more on gaining knowledge rather than just prediction. Data preprocessing techniques like cleaning, integration, and transformation are used to engineer the input data. Data postprocessing techniques combine multiple models to engineer the output.
A data warehouse is a subject-oriented, integrated, time-variant collection of data that supports management's decision-making processes. It contains data extracted from various operational databases and data sources. The data is cleaned, transformed, integrated and loaded into the data warehouse for analysis. A data warehouse uses a multidimensional model with facts and dimensions to allow for complex analytical and ad-hoc queries from multiple perspectives. It is separately administered from operational databases to avoid impacting transaction processing systems and allow optimized access for decision support.
This document provides an overview of classification and prediction evaluation techniques. It discusses evaluating models on large and small datasets using techniques like train/test splits, cross-validation, and the bootstrap method. Evaluation measures for binary classification like precision, recall, and accuracy are presented. Visualization techniques like lift charts and ROC curves for comparing model performance are also introduced.
The document provides an overview of data warehousing and OLAP technology. It defines a data warehouse as a subject-oriented, integrated collection of historical data used for analysis and decision making. It describes key properties of data warehouses including being subject-oriented, integrated, time-variant, and non-volatile. It also discusses dimensional modeling, data cubes, and OLAP for analyzing aggregated data.
This document provides a summary of lecture 5 on association rule mining. It discusses topics like association rule mining, mining single and multilevel association rules, measurements like support and confidence. It provides examples of mining association rules from transactional databases and relational tables. It describes the Apriori algorithm for mining frequent itemsets and generating association rules. It also discusses techniques like FP-tree for overcoming performance issues of Apriori.
This document provides an overview of clustering techniques. It discusses what clustering is, different types of attributes that can be clustered, and major clustering approaches. The major approaches covered are partitioning algorithms, which construct partitions and evaluate them; hierarchical algorithms, which create a hierarchical decomposition; and density-based algorithms, which are based on connectivity and density. Examples of applications are also provided.
The document discusses data mining and provides an overview of key concepts. It describes data mining as the process of discovering patterns in large data sets involving techniques like classification, clustering, association rule mining, and outlier detection. It also discusses different types of data that can be mined, including transactional data and text data. Additionally, it presents different classifications of data mining systems based on the type of data, knowledge discovered, and techniques used.
This document introduces an online course on data warehousing from Edureka. It provides an overview of key topics that will be covered in the course, including what a data warehouse is, its architecture, the ETL process, and modeling dimensions and facts. It also shows examples of using PostgreSQL to create tables and Talend to populate them as part of a hands-on project in the course. The course modules will cover data warehousing introduction, dimensions and facts, normalization, modeling, ETL concepts, and a project building a data warehouse using Talend.
Apache kylin 2.0: from classic olap to real-time data warehouseYang Li
Apache Kylin, which started as a big data OLAP engine, is reaching its v2.0. Yang Li explains how, armed with snowflake schema support, a full SQL interface, spark cubing, and the ability to consume real-time streaming data, Apache Kylin is closing the gap to becoming a real-time data warehouse.
Talks about best practices and patterns on how to design an efficient cube in Kylin. Covers concepts like mandatory dimension, hierarchy dimension, derived dimension, incremental build, aggregation group etc.
The document discusses data warehouse implementation and online analytical processing (OLAP). It describes the compute cube operator, which computes aggregates for all subsets of specified dimensions. It also covers efficient cube computation techniques like chunking and materialized views. Better access methods for OLAP like bitmap indexing and join indexing are also summarized. The document emphasizes that efficient query processing requires determining which operations to perform on available cuboids and selecting the optimal cuboid based on factors like storage size and indexing.
Apache Kylin’s Performance Boost from Apache HBaseHBaseCon
Hongbin Ma and Luke Han (Kyligence)
Apache Kylin is an open source distributed analytics engine that provides a SQL interface and multi-dimensional analysis on Hadoop supporting extremely large datasets. In the forthcoming Kylin release, we optimized query performance by exploring the potentials of parallel storage on top of HBase. This talk explains how that work was done.
A data warehouse is a central repository of historical data from an organization's various sources designed for analysis and reporting. It contains integrated data from multiple systems optimized for querying and analysis rather than transactions. Data is extracted, cleaned, and loaded from operational sources into the data warehouse periodically. The data warehouse uses a dimensional model to organize data into facts and dimensions for intuitive analysis and is optimized for reporting rather than transaction processing like operational databases. Data warehousing emerged to meet the growing demand for analysis that operational systems could not support due to impacts on performance and limitations in reporting capabilities.
The document provides an overview of structured query language (SQL) and SQL injection. It discusses SQL queries including SELECT, INSERT, UPDATE, DELETE statements. It also covers identifying the database platform, combining rows, SQL injection cheat sheets, bypassing input validation filters, and troubleshooting SQL injection attacks on various platforms including PostgreSQL, DB2, Informix and Ingres.
This document discusses decision support systems (DSS) and data warehousing. It provides definitions of DSS as interactive computer-based systems that help decision makers use data and models to identify and solve problems. It also defines data warehousing as a subject-oriented, integrated, nonvolatile, and time-variant collection of data used to support management decisions. The document outlines the concepts of operational databases, data warehousing architectures, and multidimensional database structures.
The document provides an overview of the key components and considerations for building a data warehouse. It discusses 7 main components: 1) the data warehouse database, 2) sourcing, acquisition, cleanup and transformation tools, 3) metadata, 4) access (query) tools, 5) data marts, 6) data warehouse administration and management, and 7) information delivery systems. It also outlines important design considerations, technical considerations, and implementation considerations that must be addressed when building a data warehouse environment.
An Overview On Data Warehousing An Overview On Data WarehousingBRNSSPublicationHubI
The document provides an overview of data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data designed for query and analysis rather than transactions. Data warehouses separate analysis from transactions and consolidate data from multiple sources to help with decision making and maintaining historical records. Key features of data warehouses include being subject-oriented, integrated, time-variant, and non-volatile. Data marts are focused data warehouses for a single subject area like sales or finance. Online analytical processing (OLAP) tools are used to interactively analyze multidimensional data in data warehouses.
This document provides information about a course on data warehousing and data mining, including:
1. It outlines the course syllabus which covers the basics of data warehousing, data preprocessing, association rules, classification and clustering, and recent trends in data mining.
2. It describes the 5 units that make up the course, including an overview of the topics covered in each unit such as data warehouse architecture, data integration, decision trees, and applications of data mining.
3. It lists two textbooks and four references that will be used for the course.
This document discusses data warehousing and OLAP technology. It defines key concepts like data warehouses, OLTP vs OLAP, and multidimensional data models. It also explains data warehouse architectures like star schemas and snowflake schemas, and how dimensions and measures are modeled in a data cube.
The Big Data Importance – Tools and their UsageIRJET Journal
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Introduction to Data Warehousing
1. Prof. S. K. Pandey, I.T.S, Ghaziabad
Data Warehousing & Mining
UNIT – I
2. Prof. S.K. Pandey, I.T.S, Ghaziabad 2
Syllabus of Unit - I
DSS-Uses, definition, Operational Database.
Introduction to DATA Warehousing. Data-Mart,
Concept of Data-Warehousing,
Multi Dimensional Database Structures.
Client/Server Computing Model & Data
Warehousing
Parallel Processors & Cluster Systems. Distributed
DBMS implementations.
3. Introduction – Decision Support
System (DSS)
A Decision Support System (DSS) is an interactive computer-
based system or subsystem intended to help decision makers use
communications technologies, data, documents, knowledge
and/or models to identify and solve problems, complete decision
process tasks, and make decisions.
It is clear that DSS belong to an environment with
multidisciplinary foundations, including (but not exclusively):
– Database research,
– Artificial intelligence,
– Human-computer interaction,
– Simulation methods,
– Software engineering, and
– Telecommunications.
Prof. S.K. Pandey, I.T.S, Ghaziabad 3
4. Prof. S.K. Pandey, I.T.S, Ghaziabad 4
DSS
• A Decision Support System (DSS) is a computer-
based information system that supports business or
organizational decision-making activities.
• DSSs serve the management, operations, and planning
levels of an organization (usually mid and higher
management) and help to make decisions, which may
be rapidly changing and not easily specified in advance
(Unstructured and Semi-Structured decision problems).
• Decision support systems can be either fully
computerized, human or a combination of both.
6. Prof. S.K. Pandey, I.T.S, Ghaziabad 6
Typical DSS Architecture
TPS: transaction
processing system
MODEL: representation
of a problem
OLAP: on-line analytical
processing
USER INTERFACE:
how user enters problem
& receives answers
DSS DATABASE:
current data from
applications or groups
DATA MINING:
technology for finding
relationships in large data
bases for prediction
TPS
EXTERNAL
DATA
DSS DATA
BASE
DSS SOFTWARE SYSTEM
MODELS
OLAP TOOLS
DATA MININGTOOLS
USER
INTERFACE
USER
7. Why DSS?
Increasing complexity of decisions
– Technology
– Information:
“Data, data everywhere, and not the time to think!”
– Number and complexity of options
– Pace of change
Increasing availability of computerized support
– Inexpensive high-powered computing
– Better software
– More efficient software development process
Increasing usability of computers
Prof. S.K. Pandey, I.T.S, Ghaziabad 7
8. Prof. S.K. Pandey, I.T.S, Ghaziabad 8
Operational Databases
Operational database management systems (also referred to as OLTP
databases), are used to manage dynamic data in real-time.
These types of databases allow you to do more than simply view archived
data. Operational databases allows to modify that data (add, change or delete
data), doing it in real-time.
Since the early 90's, the operational database software market has been largely
taken over by SQL engines.
Today, the operational DBMS market (formerly OLTP) is evolving
dramatically, with new, innovative entrants and incumbents supporting the
growing use of unstructured data and NoSQL DBMS engines, as well as XML
databases and NewSQL databases.
Operational databases are increasingly supporting distributed database
architecture that provides high availability and fault tolerance through
replication and scale out ability.
10. Prof. S.K. Pandey, I.T.S, Ghaziabad 10
FEATURES DATABASE DATA WAREHOUSE
Characteristic It is based on Operational Processing. It is based on Informational Processing.
Data It mainly stores the Current data which
always guaranteed to be up-to-date.
It usually stores the Historical data whose
accuracy is maintained over time.
Function It is used for day-to-day operations. It is used for long-term informational
requirements and decision support.
User The common users are clerk, DBA,
database professional.
The common users are knowledge worker
(e.g., manager, executive, analyst)
Unit of work Its work consists of short and simple
transaction.
The operations on it consists of complex
queries..
Focus The focus is on “Data IN” The focus is on “Information OUT”
Orientation The orientation is on Transaction. The orientation is on Analysis.
DB design The designing of database is ER based
and application-oriented.
The designing is done using star/snowflake
schema and its subject-oriented.
Summarization The data is primitive and highly
detailed.
The data is summarized and in consolidated
form.
View The view of the data is flat relational. The view of the data is multidimensional.
Differences between the Databases and Data Warehouses
11. Prof. S.K. Pandey, I.T.S, Ghaziabad 11
FEATURES DATABASE DATA WAREHOUSE
Function It is used for day-to-day operations. It is used for long-term informational
requirements and decision support.
User The common users are clerk, DBA,
database professional.
The common users are knowledge worker
(e.g., manager, executive, analyst)
Access The most frequent type of access type is
read/write.
It mostly use the read access for the
stored data.
Operations The main operation is index/hash on
primary key.
For any operation it needs a lot of scans.
Number of
records accessed
A few tens of records. A bunch of millions of records.
Number of users In order of thousands. In the order of hundreds only.
DB size 100 MB to GB. 100 GB to TB.
Priority High performance, high availability High flexibility, end-user autonomy
Metric To measure the efficiency, transaction
throughput is measured.
To measure the efficiency, query
throughput and response time is
measured.
13. Prof. S.K. Pandey, I.T.S, Ghaziabad 13
DATA Warehousing - Introduction
A data warehouse is a subject-oriented,
integrated, nonvolatile, time-variant collection
of data in support of management's decisions.
- WH Inmon
15. Prof. S.K. Pandey, I.T.S, Ghaziabad 15
Data Warehouse Usage
Three kinds of data warehouse applications
– Information processing
supports querying, basic statistical analysis, and reporting using
crosstabs, tables, charts and graphs
– Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
– Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models, performing
classification and prediction, and presenting the mining results
using visualization tools.
Differences among the three tasks
16. Prof. S.K. Pandey, I.T.S, Ghaziabad 16
Data Warehouse: Subject-Oriented
Organized around major subjects, such as customer, product,
sales.
Focusing on the modeling and analysis of data for decision
makers, not on daily operations or transaction processing.
Provide a simple and concise view around particular
subject issues by excluding data that are not useful in the
decision support process.
17. Prof. S.K. Pandey, I.T.S, Ghaziabad 17
Subject-Oriented
Quotes Orders
ProspectsLeads
Operational
Data
Warehouse
Customers Products
Regions Time
Focus is on Subject Areas rather than Applications
18. Prof. S.K. Pandey, I.T.S, Ghaziabad 18
Data Warehouse—Integrated
Constructed by integrating multiple, heterogeneous data
sources
– relational databases, flat files, on-line transaction records
Data cleaning and data integration techniques are applied.
– Ensure consistency in naming conventions, encoding
structures, attribute measures, etc. among different data
sources
E.g., Hotel price: currency, tax, breakfast covered, etc.
– When data is moved to the warehouse, it is converted.
19. Prof. S.K. Pandey, I.T.S, Ghaziabad 19
Data Warehouse—Time Variant
The time horizon for the data warehouse is significantly longer
than that of operational systems.
– Operational database: current value data.
– Data warehouse data: provide information from a historical
perspective (e.g., past 5-10 years)
Every key structure in the data warehouse
– Contains an element of time, explicitly or implicitly
– But the key of operational data may or may not contain
“time element”.
20. Prof. S.K. Pandey, I.T.S, Ghaziabad 20
Time Variant
Operational
Data
Warehouse
Current Value data
• time horizon : 60-90 days
Snapshot data
• time horizon : 5-10 years
•data warehouse stores historical
data
Data Warehouse Typically Spans Across Time
21. Prof. S.K. Pandey, I.T.S, Ghaziabad 21
Data Warehouse—Non-Volatile
A physically separate store of data transformed from the
operational environment.
Operational update of data does not occur in the data
warehouse environment.
– Does not require transaction processing, recovery, and
concurrency control mechanisms
– Requires only two operations in data accessing:
initial loading of data and access of data.
22. Prof. S.K. Pandey, I.T.S, Ghaziabad 22
Non-volatile
Operational Data
Warehouse
replace
change
insert
changeinsert
delete
load
read only
access
Data Warehouse Is Relatively Static In Nature
23. Prof. S.K. Pandey, I.T.S, Ghaziabad 23
Data Warehouse vs. Heterogeneous
DBMS
Traditional heterogeneous DB integration:
– Build wrappers/mediators on top of heterogeneous databases
– Query driven approach
When a query is posed to a client site, a meta-dictionary is used to
translate the query into queries appropriate for individual
heterogeneous sites involved, and the results are integrated into a
global answer set
Complex information filtering, compete for resources
Data warehouse: update-driven, high performance
– Information from heterogeneous sources is integrated in advance and
stored in warehouses for direct query and analysis
24. Prof. S.K. Pandey, I.T.S, Ghaziabad 24
Data Warehouse vs. Operational DBMS
OLTP (on-line transaction processing)
– Major task of traditional relational DBMS
– Day-to-day operations: purchasing, inventory, banking, manufacturing,
payroll, registration, accounting, etc.
OLAP (on-line analytical processing)
– Major task of data warehouse system
– Data analysis and decision making
Distinct features (OLTP vs. OLAP):
– User and system orientation: customer vs. market
– Data contents: current, detailed vs. historical, consolidated
– Database design: ER + application vs. star + subject
– View: current, local vs. evolutionary, integrated
– Access patterns: update vs. read-only but complex queries
25. Prof. S.K. Pandey, I.T.S, Ghaziabad 25
OLTP vs. OLAP
OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
26. Prof. S.K. Pandey, I.T.S, Ghaziabad 26
Need for Data Warehousing
Better business intelligence for end-users
Reduction in time to locate, access, and analyze information
Consolidation of disparate information sources
Strategic advantage over competitors
Faster time-to-market for products and services
Replacement of older, less-responsive decision support systems
Reduction in demand on IS to generate reports
27. Prof. S.K. Pandey, I.T.S, Ghaziabad 27
Why Separate Data Warehouse?
High performance for both systems
– DBMS— tuned for OLTP: access methods, indexing,
concurrency control, recovery
– Warehouse—tuned for OLAP: complex OLAP queries,
multidimensional view, consolidation.
Different functions and different data:
– missing data: Decision support requires historical data
which operational DBs do not typically maintain
– data consolidation: DS requires consolidation
(aggregation, summarization) of data from heterogeneous
sources
– data quality: different sources typically use inconsistent
data representations, codes and formats which have to be
reconciled
28. Prof. S.K. Pandey, I.T.S, Ghaziabad 28
Data Mart
The data mart is a subset of the data warehouse that is
usually oriented to a specific business line or team. Data
marts are small slices of the data warehouse.
Whereas data warehouses have an enterprise-wide
depth, the information in data marts pertains to a single
department.
Data marts improve end-user response time by allowing
users to have access to the specific type of data they
need to view most often by providing the data in a way
that supports the collective view of a group of users.
29. Contd………….
A data mart is basically a condensed and more focused
version of a data warehouse that reflects the regulations
and process specifications of each business unit within an
organization.
Each data mart is dedicated to a specific business
function or region.
This subset of data may span across many or all of an
enterprise’s functional subject areas.
It is common for multiple data marts to be used in order
to serve the needs of each individual business unit (different
data marts can be used to obtain specific information for various enterprise
departments, such as accounting, marketing, sales, etc.).
Prof. S.K. Pandey, I.T.S, Ghaziabad 29
30. Reasons for creating a data mart
Easy access to frequently needed data
Creates collective view by a group of users
Improves end-user response time
Ease of creation
Lower cost than implementing a full data warehouse
Potential users are more clearly defined than in a full
data warehouse
Contains only business essential data and is less
cluttered.
Prof. S.K. Pandey, I.T.S, Ghaziabad 30
31. Types of Data Marts
Dependent Data Mart: A dependent data mart is one
whose source is another data warehouse, and all
dependent data marts within an organization are
typically fed by the same source — the enterprise data
warehouse.
Prof. S.K. Pandey, I.T.S, Ghaziabad 31
32. Contd…
Independent Data Mart: An independent data mart
is one whose source is directly from transactional
systems, legacy applications, or external data feeds.
Prof. S.K. Pandey, I.T.S, Ghaziabad 32
33. Prof. S.K. Pandey, I.T.S, Ghaziabad 33
Data warehouse:
i. Holds multiple subject areas
ii. Holds very detailed information
iii. Works to integrate all data sources
iv. Does not necessarily use a dimensional model but feeds dimensional
models.
Data mart:
i. Often holds only one subject area- for example, Finance, or Sales
ii. May hold more summarized data (although many hold full detail)
iii. Concentrates on integrating information from a given subject area or
set of source systems
iv. Is built focused on a dimensional model using a star schema.
Data mart vs data warehouse
34. Prof. S.K. Pandey, I.T.S, Ghaziabad
34
Multi-Tiered Architecture
Components & Framework
Data Integration Stage
35. Prof. S.K. Pandey, I.T.S, Ghaziabad 35
Multi Dimensional Database
Structures
Sales volume as a function of product,
month, and region
Product
Month
Dimensions: Product, Location, Time
Hierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
36. Prof. S.K. Pandey, I.T.S, Ghaziabad 36
From Tables and Spreadsheets
to Data Cubes
A data warehouse is based on a multidimensional data model
which views data in the form of a data cube
A data cube, such as sales, allows data to be modeled and viewed in
multiple dimensions
– Dimension tables, such as item (item_name, brand, type), or
time(day, week, month, quarter, year)
– Fact table contains measures (such as dollars_sold) and keys to
each of the related dimension tables
In data warehousing literature, an n-D base cube is called a base
cuboid. The top most 0-D cuboid, which holds the highest-level of
summarization, is called the apex cuboid. The lattice of cuboids
forms a data cube.
37. Prof. S.K. Pandey, I.T.S, Ghaziabad 37
Cube: A Lattice of Cuboids
all
time item location supplier
time,item time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,location
time,item,supplier
time,location,supplier
item,location,supplier
time, item, location, supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
39. Prof. S.K. Pandey, I.T.S, Ghaziabad 39
Conceptual Modeling of Data
Warehouses
Modeling data warehouses: dimensions & measures
– Star schema: A fact table in the middle connected to a set of
dimension tables
– Snowflake schema: A refinement of star schema where some
dimensional hierarchy is normalized into a set of smaller
dimension tables, forming a shape similar to snowflake
– Fact constellations: Multiple fact tables share dimension
tables, viewed as a collection of stars, therefore called galaxy
schema or fact constellation
40. Prof. S.K. Pandey, I.T.S, Ghaziabad 40
Example of Star Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
province_or_street
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch
41. Prof. S.K. Pandey, I.T.S, Ghaziabad 41
Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
item
branch_key
branch_name
branch_type
branch
supplier_key
supplier_type
supplier
city_key
city
province_or_street
country
city
42. Prof. S.K. Pandey, I.T.S, Ghaziabad 42
Example of Fact Constellation
time_key
day
day_of_the_week
month
quarter
year
time
location_key
street
city
province_or_street
country
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_type
item
branch_key
branch_name
branch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_key
shipper_name
location_key
shipper_type
shipper
43. Prof. S.K. Pandey, I.T.S, Ghaziabad 43
Client/Server Computing Model &
Data Warehousing
The fundamental characteristic of client/server computing is
distribution of computing resources (e.g. data, compute power)
across different computers.
The idea is to divide applications into logical segments (tasks) so
that they are then performed on platforms most appropriate.
A client/server database system increases processing power by
separating the database management system from the application;
the client as the front-end system handling the user interface and
the server as the back-end system accessing the database, which
cooperate to run an application.
44. Contd….
Data Warehousing is a continual process which
enables a corporation to assemble operational and
other data from a variety of internal and external
sources, and transform that data into consistent,
high-quality, business information, distribute that
information to the points of maximum value within
the organizations, and provide easy, flexible and fast
access for busy non-technical users.
Prof. S.K. Pandey, I.T.S, Ghaziabad 44
45. Reasons for using client/server
Exploitation of centralised computing power /data
capacity
Scalability
Performance
Flexibility (in order to adjust to changing demands)
GUI on desktop
Protection of investment, strategic software, strategic
data
Client/server provides an integrated solution.
Prof. S.K. Pandey, I.T.S, Ghaziabad 45
46. Prof. S.K. Pandey, I.T.S, Ghaziabad 46
Parallel Processors & Cluster
Systems
47. Prof. S.K. Pandey, I.T.S, Ghaziabad 47
Loosely Coupled - Clusters
Collection of independent whole uni-processors or SMPs
– Usually called nodes
Interconnected to form a cluster
Working together as unified resource
– Illusion of being one machine
Communication via fixed path or network connections
Cluster Benefits
Absolute scalability
Incremental scalability
High availability
Superior price/performance
48. Prof. S.K. Pandey, I.T.S, Ghaziabad 48
Distributed DBMS implementations
What Is A Distributed DBMS?
Decentralization of business operations and globalization of
businesses created a demand for distributing the data and processes
across multiple locations.
Distributed database management systems (DDBMS) are designed
to meet the information requirements of such multi-location
organizations.
A DDBMS manages the storage and processing of logically
related data over interconnected computer systems in which
both data and processing functions are distributed among several
sites.
Distributed processing shares the database’s logical processing
among two or more physically independent sites that are
connected through a network.
49. DDBMS Advantages
Data located near site with greatest demand
Faster data access
Faster data processing
Growth facilitation
Improved communications
Reduced operating costs
User-friendly interface
Less danger of single-point failure
Processor independence
Prof. S.K. Pandey, I.T.S, Ghaziabad 49
50. Prof. S.K. Pandey, I.T.S, Ghaziabad 50
Distributed Processing
Shares database’s logical processing among physically, networked independent sites
51. Prof. S.K. Pandey, I.T.S, Ghaziabad 51
DDBMS Components
Computer workstations that form the network
system.
Network hardware and software components that
reside in each workstation.
Communications media that carry the data from one
workstation to another.
Transaction processor (TP) receives and processes
the application’s data requests.
Data processor (DP) stores and retrieves data
located at the site. Also known as data manager
(DM).
52. Prof. S.K. Pandey, I.T.S, Ghaziabad 52
Distributed DB Transparency
A DDBMS ensures that the database operations are
transparent to the end user.
Different types of transparencies are:
– Distribution transparency
– Transaction transparency
– Failure transparency
– Performance transparency
– Heterogeneity transparency
53. 53
Distributed Database Design
All design principles and concepts discussed in the
context of a centralized database also apply to a
distributed database.
Three additional issues are relevant to the design
of a distributed database:
– data fragmentation
– data replication
– data allocation
Prof. S.K. Pandey, I.T.S, Ghaziabad
54. 54
Data Fragmentation
Data fragmentation allows us to break a single object (a
database or a table) into two or more fragments.
Three type of fragmentation strategies are available
to distribute a table: - Horizontal, Vertical, Mixed.
Horizontal fragmentation divides a table into
fragments consisting of sets of tuples:
– Each fragment has unique rows and is stored at a
different node
– Example: A bank may distribute its customer table
by location
Prof. S.K. Pandey, I.T.S, Ghaziabad
55. 55
Contd……
Vertical fragmentation divides a table into fragments
consisting of sets of columns
– Each fragment is located at a different node and
consists of unique columns - with the exception of
the primary key column, which is common to all
fragments
– Example: The Customer table may be divided into
two fragments, one fragment consisting of Cust ID,
name, and address may be located in the Service
building and the other fragment with Cust ID, credit
limit, balance, dues may be located in the Collection
building.
Prof. S.K. Pandey, I.T.S, Ghaziabad
56. 56
Data Fragmentation
Mixed fragmentation combines the horizontal and
vertical strategies.
A fragment may consist of a subset of rows and a
subset of columns of the original table.
Example: Customer table may be divided by state
and grouped by columns. The service building in
Texas will store Customer service related
information for customers from Texas.
Prof. S.K. Pandey, I.T.S, Ghaziabad
57. 57
Data Replication
Data replication involves storing multiple copies of a
fragment in different locations. For example, a copy
may be stored in New Delhi and another in Mumbai.
It improves response time and data availability.
Data replication requires the DDBMS to maintain data
consistency among the replicas.
A fully replicated database stores multiple copies of each
database fragment.
A partially replicated database stores multiple copies of
some database fragments at multiple sites.
Prof. S.K. Pandey, I.T.S, Ghaziabad
58. 58
Data Allocation
Data allocation decision involves determining the
location of the fragments so as to achieve the design
goals of cost, response time and availability.
Three data allocation strategies are: centralized,
partitioned and replicated.
A centralized allocation strategy stores the entire
database in a single location.
A partitioned strategy divides the database into
disjointed parts (fragments) and allocates the fragments
to different locations.
In a replicated strategy copies of one or more database
fragments are stored at several sites.
Prof. S.K. Pandey, I.T.S, Ghaziabad