The document discusses efficient algorithms for mining non-redundant recurrent rules from sequence databases. It begins with an introduction of recurrent rules and their representation of temporal constraints repeated across sequences. Several algorithms are then presented for mining non-redundant recurrent rules, including the original NR3 algorithm, parallel and optimized variants of NR3, and a bidirectional approach. The document concludes with a discussion of interleaving bidirectional mining for further improvements.
The document discusses deadlocks in operating systems. It defines deadlock as when a process waits for a resource held by another waiting process, forming a circular chain of processes waiting for each other. It characterizes deadlock by the conditions of mutual exclusion, hold and wait, no preemption, and circular wait. The document outlines strategies to handle deadlocks through prevention, avoidance, and detection and recovery. It describes resource allocation graphs to model deadlocks and the conditions for deadlocks using AND and OR resource allocation. Finally, it discusses different techniques for deadlock prevention, detection, and recovery in a system.
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
Combined Approach to Deadlock Handling
This document discusses deadlock avoidance techniques. It explains the concepts of safe and unsafe states when allocating resources to processes. The resource allocation graph algorithm uses claim and assignment edges to model potential resource requests. Banker's algorithm requires processes to declare maximum resource needs upfront. It uses an allocation matrix and need matrix to determine if allocating resources to a process will result in an unsafe state. An example demonstrates tracking available resources and determining if processes can safely obtain requested resources without causing deadlock.
This document discusses operating system topics related to deadlocks, including definitions, properties, prevention, detection, and recovery from deadlocks. It defines deadlock as when a process requests resources that are held by another waiting process, creating a circular wait. Four conditions must be met for deadlock: mutual exclusion, hold and wait, no preemption, and circular wait. Techniques to prevent deadlocks include deadlock avoidance using safe states and resource allocation graphs, and deadlock prevention by ensuring one of the four conditions is never satisfied. Deadlock detection uses wait-for graphs or detection algorithms, and recovery options are terminating processes or preempting resources.
Senthilkanth,MCA..
The following ppt's full topic covers Operating System for BSc CS, BCA, MSc CS, MCA students..
1.Introduction
2.OS Structures
3.Process
4.Threads
5.CPU Scheduling
6.Process Synchronization
7.Dead Locks
8.Memory Management
9.Virtual Memory
10.File system Interface
11.File system implementation
12.Mass Storage System
13.IO Systems
14.Protection
15.Security
16.Distributed System Structure
17.Distributed File System
18.Distributed Co Ordination
19.Real Time System
20.Multimedia Systems
21.Linux
22.Windows
This document discusses deadlocks in operating systems. It defines deadlock as when a set of blocked processes each hold a resource and wait for a resource held by another process. It then covers methods for handling deadlocks such as prevention, avoidance, detection, and recovery. Prevention ensures deadlock conditions cannot occur. Avoidance allows the system to deny requests that could lead to deadlock. Detection identifies when a deadlock has occurred. Recovery breaks deadlocks by terminating or preempting processes.
There are three main approaches to handling deadlocks: prevention, avoidance, and detection with recovery. Prevention methods constrain how processes request resources to ensure at least one necessary condition for deadlock cannot occur. Avoidance requires advance knowledge of processes' resource needs to decide if requests can be immediately satisfied. Detection identifies when a deadlocked state occurs and recovers by undoing the allocation that caused it.
A document about deadlocks in operating systems is summarized as follows:
1. A deadlock occurs when a set of processes form a circular chain where each process is waiting for a resource held by the next process in the chain. The four conditions for deadlock are mutual exclusion, hold and wait, no preemption, and circular wait.
2. Deadlocks can be modeled using a resource allocation graph where processes and resources are vertices and edges represent resource requests. A cycle in the graph indicates a potential deadlock.
3. Methods for handling deadlocks include prevention, avoidance, and detection/recovery. Prevention ensures deadlock conditions cannot occur while avoidance allows the system to dynamically verify new allocations will not
The document discusses deadlocks in operating systems. It defines deadlock as when a process waits for a resource held by another waiting process, forming a circular chain of processes waiting for each other. It characterizes deadlock by the conditions of mutual exclusion, hold and wait, no preemption, and circular wait. The document outlines strategies to handle deadlocks through prevention, avoidance, and detection and recovery. It describes resource allocation graphs to model deadlocks and the conditions for deadlocks using AND and OR resource allocation. Finally, it discusses different techniques for deadlock prevention, detection, and recovery in a system.
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
Combined Approach to Deadlock Handling
This document discusses deadlock avoidance techniques. It explains the concepts of safe and unsafe states when allocating resources to processes. The resource allocation graph algorithm uses claim and assignment edges to model potential resource requests. Banker's algorithm requires processes to declare maximum resource needs upfront. It uses an allocation matrix and need matrix to determine if allocating resources to a process will result in an unsafe state. An example demonstrates tracking available resources and determining if processes can safely obtain requested resources without causing deadlock.
This document discusses operating system topics related to deadlocks, including definitions, properties, prevention, detection, and recovery from deadlocks. It defines deadlock as when a process requests resources that are held by another waiting process, creating a circular wait. Four conditions must be met for deadlock: mutual exclusion, hold and wait, no preemption, and circular wait. Techniques to prevent deadlocks include deadlock avoidance using safe states and resource allocation graphs, and deadlock prevention by ensuring one of the four conditions is never satisfied. Deadlock detection uses wait-for graphs or detection algorithms, and recovery options are terminating processes or preempting resources.
Senthilkanth,MCA..
The following ppt's full topic covers Operating System for BSc CS, BCA, MSc CS, MCA students..
1.Introduction
2.OS Structures
3.Process
4.Threads
5.CPU Scheduling
6.Process Synchronization
7.Dead Locks
8.Memory Management
9.Virtual Memory
10.File system Interface
11.File system implementation
12.Mass Storage System
13.IO Systems
14.Protection
15.Security
16.Distributed System Structure
17.Distributed File System
18.Distributed Co Ordination
19.Real Time System
20.Multimedia Systems
21.Linux
22.Windows
This document discusses deadlocks in operating systems. It defines deadlock as when a set of blocked processes each hold a resource and wait for a resource held by another process. It then covers methods for handling deadlocks such as prevention, avoidance, detection, and recovery. Prevention ensures deadlock conditions cannot occur. Avoidance allows the system to deny requests that could lead to deadlock. Detection identifies when a deadlock has occurred. Recovery breaks deadlocks by terminating or preempting processes.
There are three main approaches to handling deadlocks: prevention, avoidance, and detection with recovery. Prevention methods constrain how processes request resources to ensure at least one necessary condition for deadlock cannot occur. Avoidance requires advance knowledge of processes' resource needs to decide if requests can be immediately satisfied. Detection identifies when a deadlocked state occurs and recovers by undoing the allocation that caused it.
A document about deadlocks in operating systems is summarized as follows:
1. A deadlock occurs when a set of processes form a circular chain where each process is waiting for a resource held by the next process in the chain. The four conditions for deadlock are mutual exclusion, hold and wait, no preemption, and circular wait.
2. Deadlocks can be modeled using a resource allocation graph where processes and resources are vertices and edges represent resource requests. A cycle in the graph indicates a potential deadlock.
3. Methods for handling deadlocks include prevention, avoidance, and detection/recovery. Prevention ensures deadlock conditions cannot occur while avoidance allows the system to dynamically verify new allocations will not
This document discusses deadlock in computer systems. It defines deadlock as when a set of processes are blocked waiting for resources held by other processes in the set. Four necessary conditions for deadlock to occur are outlined: mutual exclusion, hold and wait, no preemption, and circular wait. Strategies to handle deadlock such as prevention, detection, avoidance, and an integrated approach combining multiple strategies are also discussed. Examples of different resource types that can be involved in deadlock are provided.
This document discusses deadlocks and techniques for handling them. It begins by defining the four necessary conditions for a deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. It then describes three approaches to handling deadlocks: prevention, avoidance, and detection and recovery. Prevention aims to ensure one of the four conditions never holds. Avoidance uses more information to determine if a request could lead to a deadlock. Detection and recovery allows deadlocks but detects and recovers from them after the fact. The document provides examples of different prevention techniques like limiting resource types that can be held, ordering resource types, and preemption. It also explains the banker's algorithm for deadlock avoidance.
This document summarizes key concepts related to deadlock avoidance in operating systems. It discusses the four conditions for deadlock, describes the concept of a safe state for resource allocation, and provides an example of modeling resource allocation using a resource allocation graph. The document is presented as part of a course on operating systems, covering topics such as deadlock avoidance, safe state determination, and modeling resource allocation through graphs.
The document discusses different methods for handling deadlocks in a system. It describes deadlock characterization including the necessary conditions for deadlock, using a resource-allocation graph to model deadlocks, and examples of such graphs. It also explains several methods for handling deadlocks including deadlock prevention, avoidance, and detection and recovery. Deadlock prevention methods aim to enforce constraints to ensure the necessary conditions for deadlock cannot occur. Deadlock avoidance uses additional information to dynamically monitor the system state and ensure it remains in a safe state where deadlocks cannot happen.
The Deadlock Problem
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
Deadlock Detection in Distributed SystemsDHIVYADEVAKI
Â
The document discusses deadlocks in computing systems. It defines deadlocks and related concepts like livelock and starvation. It presents various approaches to deal with deadlocks including detection and recovery, avoidance through runtime checks, and prevention by restricting resource requests. Graph-based algorithms are described for detecting and preventing deadlocks by analyzing resource allocation graphs. The Banker's algorithm is introduced as a static prevention method. Finally, it discusses ways to eliminate the conditions required for deadlocks, like mutual exclusion, hold-and-wait, and circular wait.
The document discusses deadlocks in computer systems. It defines deadlock, presents examples, and describes four conditions required for deadlock to occur. Several methods for handling deadlocks are discussed, including prevention, avoidance, detection, and recovery. Prevention methods aim to ensure deadlocks never occur, while avoidance allows the system to dynamically prevent unsafe states. Detection identifies when the system is in a deadlocked state.
This document discusses deadlocks in computer systems. It defines a deadlock as a set of blocked processes where each process is holding a resource and waiting for a resource held by another process in the set, resulting in circular waiting. It presents examples of deadlock situations and describes the conditions required for deadlock, including mutual exclusion, hold and wait, no preemption, and circular wait. Methods for handling deadlocks include prevention, avoidance, and detection and recovery. Prevention ensures deadlocks never occur through restrictions, while avoidance uses online algorithms to ensure the system remains in a safe state where deadlocks cannot arise.
The document discusses various techniques for handling deadlocks in computer systems, including deadlock prevention, avoidance, detection, and recovery. It defines the four necessary conditions for deadlock, and describes resource-allocation graphs and wait-for graphs that can be used to model deadlock states. Detection algorithms periodically check the resource-allocation graph for cycles that indicate a deadlock. Upon detection, various recovery techniques can be used like terminating processes, preempting resources, or rolling back to a previous safe state. An optimal approach combines prevention, avoidance and detection tailored for each resource class.
Deadlocks occur when a set of blocked processes each hold resources and wait for resources held by other processes in the set, resulting in a circular wait. The four necessary conditions for deadlock are: mutual exclusion, hold and wait, no preemption, and circular wait. The banker's algorithm is a deadlock avoidance technique that requires processes to declare maximum resource needs upfront. It ensures the system is always in a safe state by delaying resource requests that could lead to an unsafe state where deadlock is possible.
The document discusses histograms in Oracle's cost-based optimizer (CBO). Histograms help improve cardinality estimates when data is skewed, leading to better query plans. They were introduced in Oracle 8 and are now automatically collected, with the number of buckets and type (frequency or height balanced) depending on the number of distinct values. The document provides background on histograms and how the CBO uses them to estimate selectivity and cardinality.
The implementation of Banker's algorithm, data structure and its parserMatthew Chang
Â
The document summarizes a student project presentation on implementing Banker's algorithm for deadlock avoidance. It includes:
1) An overview of Banker's algorithm and how it checks for safe states to avoid deadlock by dynamically evaluating resource requests.
2) Details on the data structures and parsing approach used to represent the banker's algorithm information and process resource requests.
3) A demonstration of the program loading sample data from a file, displaying the banker's algorithm information, and handling different user commands and error scenarios.
This document summarizes a chapter on deadlocks from an operating systems textbook. It defines deadlock as when a set of blocked processes wait for resources held by each other. Four conditions must be met for deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. Methods to handle deadlocks include prevention, avoidance, detection, and recovery. Prevention ensures deadlocks cannot occur by restricting resource usage. Avoidance dynamically checks the system state remains safe to prevent deadlocks. Detection allows deadlocks but recovers the system. Recovery options are terminating processes or preempting resources.
This document discusses various approaches to handling deadlocks in operating systems, including deadlock prevention, avoidance, detection, and recovery. It describes the four necessary conditions for deadlock, and models the problem using resource allocation graphs. Prevention methods aim to enforce constraints to ensure deadlock cannot occur, while avoidance algorithms dynamically monitor the system state to guarantee safety. Detection algorithms periodically search allocation graphs or wait-for graphs to find cycles indicating deadlock. Recovery requires rolling back processes to free locked resources.
The document summarizes different approaches to handling deadlocks in operating systems, including prevention, avoidance, detection, and recovery. It describes the four conditions required for deadlock, and models for representing resource allocation and processes waiting for resources, such as resource allocation graphs and wait-for graphs. Detection algorithms allow the system to enter a deadlocked state and then identify cycles in wait-for graphs to detect deadlocks.
This document discusses deadlocks in operating systems. A deadlock occurs when a set of processes are blocked waiting for resources held by each other in a cyclic manner. Four conditions must be met for a deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. The document outlines strategies for detecting and avoiding deadlocks such as deadlock detection algorithms, safe state models like the Banker's Algorithm, and techniques for preventing the four deadlock conditions.
This document discusses deadlocks, which occur when two or more processes wait indefinitely for each other to release resources. The four conditions for deadlock are outlined: mutual exclusion, hold and wait, no preemption, and circular wait. Strategies to address deadlocks include detection and recovery, avoidance, and prevention. Detection involves building a resource graph to identify deadlocks, then killing processes to break cycles. Avoidance analyzes requests to grant resources in a safe order. Prevention eliminates conditions like making all resources shareable.
Deadlock is a situation where a set of processes are blocked because each process is holding a resource and waiting for another resource acquired by some other process.
Mutual Exclusion: One or more than one resource are non-sharable (Only one process can use at a time)
This document provides an overview of Active Session History (ASH), which is an Oracle database feature that samples database session states over time. It summarizes that ASH takes snapshots of active session states, including session details and wait events, and stores this data in memory and disks. It also outlines how ASH data can be used to estimate database time usage, identify tuning opportunities, and troubleshoot session issues. The document discusses the key concepts of how ASH works, the dimensions of data that are sampled, and how parameters can control the sampling process.
This document discusses deadlock in computer systems. It defines deadlock as when a set of processes are blocked waiting for resources held by other processes in the set. Four necessary conditions for deadlock to occur are outlined: mutual exclusion, hold and wait, no preemption, and circular wait. Strategies to handle deadlock such as prevention, detection, avoidance, and an integrated approach combining multiple strategies are also discussed. Examples of different resource types that can be involved in deadlock are provided.
This document discusses deadlocks and techniques for handling them. It begins by defining the four necessary conditions for a deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. It then describes three approaches to handling deadlocks: prevention, avoidance, and detection and recovery. Prevention aims to ensure one of the four conditions never holds. Avoidance uses more information to determine if a request could lead to a deadlock. Detection and recovery allows deadlocks but detects and recovers from them after the fact. The document provides examples of different prevention techniques like limiting resource types that can be held, ordering resource types, and preemption. It also explains the banker's algorithm for deadlock avoidance.
This document summarizes key concepts related to deadlock avoidance in operating systems. It discusses the four conditions for deadlock, describes the concept of a safe state for resource allocation, and provides an example of modeling resource allocation using a resource allocation graph. The document is presented as part of a course on operating systems, covering topics such as deadlock avoidance, safe state determination, and modeling resource allocation through graphs.
The document discusses different methods for handling deadlocks in a system. It describes deadlock characterization including the necessary conditions for deadlock, using a resource-allocation graph to model deadlocks, and examples of such graphs. It also explains several methods for handling deadlocks including deadlock prevention, avoidance, and detection and recovery. Deadlock prevention methods aim to enforce constraints to ensure the necessary conditions for deadlock cannot occur. Deadlock avoidance uses additional information to dynamically monitor the system state and ensure it remains in a safe state where deadlocks cannot happen.
The Deadlock Problem
System Model
Deadlock Characterization
Methods for Handling Deadlocks
Deadlock Prevention
Deadlock Avoidance
Deadlock Detection
Recovery from Deadlock
Deadlock Detection in Distributed SystemsDHIVYADEVAKI
Â
The document discusses deadlocks in computing systems. It defines deadlocks and related concepts like livelock and starvation. It presents various approaches to deal with deadlocks including detection and recovery, avoidance through runtime checks, and prevention by restricting resource requests. Graph-based algorithms are described for detecting and preventing deadlocks by analyzing resource allocation graphs. The Banker's algorithm is introduced as a static prevention method. Finally, it discusses ways to eliminate the conditions required for deadlocks, like mutual exclusion, hold-and-wait, and circular wait.
The document discusses deadlocks in computer systems. It defines deadlock, presents examples, and describes four conditions required for deadlock to occur. Several methods for handling deadlocks are discussed, including prevention, avoidance, detection, and recovery. Prevention methods aim to ensure deadlocks never occur, while avoidance allows the system to dynamically prevent unsafe states. Detection identifies when the system is in a deadlocked state.
This document discusses deadlocks in computer systems. It defines a deadlock as a set of blocked processes where each process is holding a resource and waiting for a resource held by another process in the set, resulting in circular waiting. It presents examples of deadlock situations and describes the conditions required for deadlock, including mutual exclusion, hold and wait, no preemption, and circular wait. Methods for handling deadlocks include prevention, avoidance, and detection and recovery. Prevention ensures deadlocks never occur through restrictions, while avoidance uses online algorithms to ensure the system remains in a safe state where deadlocks cannot arise.
The document discusses various techniques for handling deadlocks in computer systems, including deadlock prevention, avoidance, detection, and recovery. It defines the four necessary conditions for deadlock, and describes resource-allocation graphs and wait-for graphs that can be used to model deadlock states. Detection algorithms periodically check the resource-allocation graph for cycles that indicate a deadlock. Upon detection, various recovery techniques can be used like terminating processes, preempting resources, or rolling back to a previous safe state. An optimal approach combines prevention, avoidance and detection tailored for each resource class.
Deadlocks occur when a set of blocked processes each hold resources and wait for resources held by other processes in the set, resulting in a circular wait. The four necessary conditions for deadlock are: mutual exclusion, hold and wait, no preemption, and circular wait. The banker's algorithm is a deadlock avoidance technique that requires processes to declare maximum resource needs upfront. It ensures the system is always in a safe state by delaying resource requests that could lead to an unsafe state where deadlock is possible.
The document discusses histograms in Oracle's cost-based optimizer (CBO). Histograms help improve cardinality estimates when data is skewed, leading to better query plans. They were introduced in Oracle 8 and are now automatically collected, with the number of buckets and type (frequency or height balanced) depending on the number of distinct values. The document provides background on histograms and how the CBO uses them to estimate selectivity and cardinality.
The implementation of Banker's algorithm, data structure and its parserMatthew Chang
Â
The document summarizes a student project presentation on implementing Banker's algorithm for deadlock avoidance. It includes:
1) An overview of Banker's algorithm and how it checks for safe states to avoid deadlock by dynamically evaluating resource requests.
2) Details on the data structures and parsing approach used to represent the banker's algorithm information and process resource requests.
3) A demonstration of the program loading sample data from a file, displaying the banker's algorithm information, and handling different user commands and error scenarios.
This document summarizes a chapter on deadlocks from an operating systems textbook. It defines deadlock as when a set of blocked processes wait for resources held by each other. Four conditions must be met for deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. Methods to handle deadlocks include prevention, avoidance, detection, and recovery. Prevention ensures deadlocks cannot occur by restricting resource usage. Avoidance dynamically checks the system state remains safe to prevent deadlocks. Detection allows deadlocks but recovers the system. Recovery options are terminating processes or preempting resources.
This document discusses various approaches to handling deadlocks in operating systems, including deadlock prevention, avoidance, detection, and recovery. It describes the four necessary conditions for deadlock, and models the problem using resource allocation graphs. Prevention methods aim to enforce constraints to ensure deadlock cannot occur, while avoidance algorithms dynamically monitor the system state to guarantee safety. Detection algorithms periodically search allocation graphs or wait-for graphs to find cycles indicating deadlock. Recovery requires rolling back processes to free locked resources.
The document summarizes different approaches to handling deadlocks in operating systems, including prevention, avoidance, detection, and recovery. It describes the four conditions required for deadlock, and models for representing resource allocation and processes waiting for resources, such as resource allocation graphs and wait-for graphs. Detection algorithms allow the system to enter a deadlocked state and then identify cycles in wait-for graphs to detect deadlocks.
This document discusses deadlocks in operating systems. A deadlock occurs when a set of processes are blocked waiting for resources held by each other in a cyclic manner. Four conditions must be met for a deadlock to occur: mutual exclusion, hold and wait, no preemption, and circular wait. The document outlines strategies for detecting and avoiding deadlocks such as deadlock detection algorithms, safe state models like the Banker's Algorithm, and techniques for preventing the four deadlock conditions.
This document discusses deadlocks, which occur when two or more processes wait indefinitely for each other to release resources. The four conditions for deadlock are outlined: mutual exclusion, hold and wait, no preemption, and circular wait. Strategies to address deadlocks include detection and recovery, avoidance, and prevention. Detection involves building a resource graph to identify deadlocks, then killing processes to break cycles. Avoidance analyzes requests to grant resources in a safe order. Prevention eliminates conditions like making all resources shareable.
Deadlock is a situation where a set of processes are blocked because each process is holding a resource and waiting for another resource acquired by some other process.
Mutual Exclusion: One or more than one resource are non-sharable (Only one process can use at a time)
This document provides an overview of Active Session History (ASH), which is an Oracle database feature that samples database session states over time. It summarizes that ASH takes snapshots of active session states, including session details and wait events, and stores this data in memory and disks. It also outlines how ASH data can be used to estimate database time usage, identify tuning opportunities, and troubleshoot session issues. The document discusses the key concepts of how ASH works, the dimensions of data that are sampled, and how parameters can control the sampling process.
The document summarizes the LASH algorithm for mining sequential patterns from sequence data with hierarchies. LASH extends traditional sequential pattern mining to handle hierarchies among items. It first defines how sequences can be generalized based on item hierarchies. It then partitions the sequence database based on the most frequent items and mines generalized patterns within each partition. Key steps include identifying relevant items, generalizing sequences, and representing equivalent sequences compactly to efficiently find all frequent generalized sequences satisfying maximum length and gap constraints.
Graph based Approach and Clustering of Patterns (GACP) for Sequential Pattern...AshishDPatel1
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The sequential pattern mining generates the sequential patterns. It can be used as the input of another program for retrieving the information from the large collection of data. It requires a large amount of memory as well as numerous I/O operations. Multistage operations reduce the efficiency of the
algorithm. The given GACP is based on graph representation and avoids recursively reconstructing intermediate trees during the mining process. The algorithm also eliminates the need of repeatedly scanning the database. A graph used in GACP is a data structure accessed starting at its first node called root and each node of a graph is either a leaf or an interior node. An interior node has one or more child nodes, thus from the root to any node in the graph defines a sequence. After construction of the graph the pruning technique called clustering is used to retrieve the records from the graph. The algorithm can be used to mine the database using compact memory based data structures and cleaver pruning methods.
In this paper, we have proposed a novel sequential mining method. The method is fast in comparison to existing method. Data mining, that is additionally cited as knowledge discovery in databases, has been recognized because the method of extracting non-trivial, implicit, antecedently unknown, and probably helpful data from knowledge in databases. The information employed in the mining method usually contains massive amounts of knowledge collected by computerized applications. As an example, bar-code readers in retail stores, digital sensors in scientific experiments, and alternative automation tools in engineering typically generate tremendous knowledge into databases in no time. Not to mention the natively computing- centric environments like internet access logs in net applications. These databases therefore work as rich and reliable sources for information generation and verification. Meanwhile, the massive databases give challenges for effective approaches for information discovery.
Mining Top-k Closed Sequential Patterns in Sequential Databases IOSR Journals
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Abstract: In data mining community, sequential pattern mining has been studied extensively. Most studies
require the specification of minimum support threshold to mine the sequential patterns. However, it is difficult
for users to provide an appropriate threshold in practice. To overcome this, we propose mining top-k closed
sequential patterns of length no less than min_l, where k is the number of closed sequential patterns to be
mined, and min_l is the minimum length of each pattern. We mine closed patterns since they are solid
representations of frequent patterns.
Keywords: closed pattern, data mining, sequential pattern, scalability
The document discusses neural networks and deep learning. It covers the machine learning paradigm for neural networks, using backpropagation for learning, and implementing neural network modules. Neural networks are described as hierarchical functions that are optimized using gradient descent. The key steps are collecting data, defining a model, making predictions using forward propagation, evaluating predictions, and updating the model using backpropagation.
Foundation and Synchronization of the Dynamic Output Dual Systemsijtsrd
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In this paper, the synchronization problem of the dynamic output dual systems is firstly introduced and investigated. Based on the time domain approach, the state variables synchronization of such dual systems can be verified. Meanwhile, the guaranteed exponential convergence rate can be accurately estimated. Finally, some numerical simulations are provided to illustrate the feasibility and effectiveness of the obtained result. Yeong-Jeu Sun "Foundation and Synchronization of the Dynamic Output Dual Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29256.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/29256/foundation-and-synchronization-of-the-dynamic-output-dual-systems/yeong-jeu-sun
Jogging While Driving, and Other Software Engineering Research Problems (invi...David Rosenblum
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invited talk presented for the Distinguished Lecturer Series of the Department of Computer Science at the University of Illinois at Chicago, 10 April 2014
Modelsâabstract and simple descriptions of some artifactâare the backbone of all software engineering activities. While writing models is hard, existing code can serve as a source for abstract descriptions of how software behaves. To infer correct usage, code analysis needs usage examples, though; the more, the better.
We have built a lightweight parser that efficiently extracts API usage models from source codeâmodels that can then be used to detect anomalies. Applied on the 200 mil- lion lines of code of the Gentoo Linux distribution, we would extract more than 15 million API constraints. On the web site checkmycode.org, anyone can check his/her code against the âwisdom of Linuxâ.
The document discusses recursion, including recursive definitions, recursive programs/algorithms, and applications of recursion. It covers key concepts such as the anatomy of a recursive call, classifying recursions by the number of recursive calls, tail recursion, and avoiding excessive recursion. Examples of recursively defined sequences, functions, and algorithms are provided to illustrate recursive concepts.
This document provides an introduction to gated recurrent units (GRUs). It begins with a recap of deep learning architectures, including standard neural networks, recurrent neural networks, and their limitations. It then provides a deep dive into GRUs, explaining how they address issues with recurrent neural networks like vanishing gradients. The document outlines a competition on rainfall prediction to showcase GRUs and discusses the training data, data preprocessing challenges, and code implementation. It concludes with a planned demo section.
The slide of the talk in http://www.meetup.com/R-Users-Sydney/events/223867196/
There is a web version here: http://wush978.github.io/FeatureHashing/index.html
This document summarizes a study on using sequential Markov chain Monte Carlo (MCMC) methods for parameter estimation of linear time-invariant (LTI) systems subjected to non-stationary seismic excitations. The study involves applying particle filtering algorithms like sequential importance sampling (SIS), sequential importance resampling (SIR), and bootstrap filtering to identify natural frequencies, mode shapes, and other parameters of single-degree-of-freedom, multi-story, and actual reinforced concrete buildings using synthetic and field acceleration data. Results show that stratified and systematic resampling give the best parameter estimates and all three particle filtering variants perform well, with identified frequencies and mode shapes close to original values.
This document introduces Julia and provides an overview of its key features. It begins with introductions and background on why Julia was created. It then covers basic Julia concepts like variables, arithmetic operators, control flow, arrays, functions, and parallelization capabilities. The document also discusses Julia's built-in package ecosystem and provides examples of packages like DataFrames. It aims to provide attendees with foundational knowledge of the Julia programming language.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Biosight: Quantitative Methods for Policy Analysis: Stochastic Dynamic Progra...IFPRI-EPTD
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This document discusses stochastic dynamic programming and its applications. It covers Bellman's principle of optimality, solving stochastic dynamic programming problems using value function iteration, and applying these concepts to agroforestry and livestock herd dynamics models. It also discusses estimating intertemporal preferences using dynamic models that relax the assumption of time-additive separability and allow for risk aversion. Examples are provided of solving a resource management problem numerically using value iteration over continuous state and control variables.
This document discusses memory models, non-blocking primitives, and lock-free algorithms for concurrent programming. It provides code examples for implementing atomic operations like set, compareAndSet, and lazySet using the Unsafe class. It evaluates the performance of different producer-consumer algorithms like spin-wait, co-operative yielding, and buffering. The document suggests buffering generally performs best by avoiding busy-waiting and allowing other threads to run. It provides references for further information on lock-free programming.
Locks? We Don't Need No Stinkin' Locks - Michael BarkerJAX London
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Embrace the dark side. As a developer you'll often be advised that writing concurrent code should be the purview of the genius coders alone. In this talk Michael Barker will discard that notion into the cesspits of logic and reason and attempt to present on the less understood area of non-blocking concurrency, i.e. concurrency without locks. We'll look the modern Intel CPU architecture, why we need a memory model, the performance costs of various non-blocking constructs and delve into the implementation details of the latest version of the Disruptor to see how non-blocking concurrency can be applied to build high performance data structures.
Similar to Mining non-redundant recurrent rules from a sequence database (20)
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
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Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
Â
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
Â
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
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Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Literature Review Basics and Understanding Reference Management.pptx
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Mining non-redundant recurrent rules from a sequence database
1. Mining Non-Redundant Recurrent Rules from a Sequence Database
Yoon SeungYong
Ministry of Science and ICT, Republic of Korea
forcom@forcom.kr
- Efficient Mining of Recurrent Rules from a Sequence Database(Lo et al., DASFAA 2008)
- Parallel Mining of Non-Redundant Recurrent Rules from a Sequence Database(Yoon and Seki, ISIS 2017)
· A Parallel Algorithm for Mining Non-Redundant Recurrent Rules from a Sequence Database(Yoon and Seki, JACIII 2019)
- Towards Efficient Mining of Non-Redundant Recurrent Rules from a Sequence Database(Yoon and Seki, IWCIA 2017)
· Mining Non-Redundant Recurrent Rules from a Sequence Database(Yoon and Seki, IJCISTUDIES 2018)
- Efficient Mining of Recurrent Rules from a Sequence Database Using Multi-Core Processors(Yoon and Seki, SCIS&ISIS 2018)
- Bidirectional Mining of Non-Redundant Recurrent Rules from a Sequence Database(Lo et al., IEEE ICDE 2011)
- A New Algorithm for Mining Recurrent Rules from a Sequence Database(Seki and Yoon, IEEE SMC 2019)
2. Table of Contents
1. Motivation
2. Mining Non-Redundant Recurrent Rules (NR3) â Lo et al.
3. Parallel Mining of Non-Redundant Recurrent Rules (pNR3)
4. Loop-Fused Mining of NR3 (LF-NR3)
5. Parallel Loop-Fused Mining of NR3 (pLF-NR3)
6. Bidirectional Mining of NR3 (BOB) â Lo et al.
7. Interleaved Bidirectional Mining of NR3 (iBiRM)
8. Conclusion
2019.11.18. 2
4. Sequence Database & Sequential Rule
ï§ Transaction Histories
ï§ Program Traces
2019.11.18. 4
Customer Movie Rental History
Alice Star Wars 4, Star Wars 5, Star Wars 6, Star Wars 1
Bob Shrek, Spirited Away, Your Name
Clara Spirited Away, Howlâs Moving Castle, Princess Mononoke
David Star Wars 1, Star Wars 2, Star Wars 3, Star Wars 4, Star Wars 5
Eve Your Name
Trace ID Command
1 check, lock, use, use, unlock, exit
2 check, lock, use, check, lock, use, unlock, exit
3 check, use, unlock, exit
4 check, lock, use
5 check, lock, use, unlock, check, lock, use, unlock, exit
ãStar Wars 4ãâ ãStar Wars 5ã
ãlockãâ ãunlockã
12. FS-Set, CS-Set, LS-Set
ï§ The set of frequent sequential pattern (FS-Set)
ï§ ð¹ð = {ð | support ð ⥠min_sup}
ï§ The set of closed frequent sequential pattern (CS-Set)
ï§ ð¶ð = {ð |ð â ð¹ð ððð âð â²
â ð¹ð, ð ð¢ðâ ð¡âðð¡ ð â ð â²
ððð support ð = support ð â²
}
ï§ Project Database Closed Set (LS-Set)
ï§ ð¿ð = {ð | support ð ⥠min_sup ððð âð â²
, ð ð¢ðâ ð¡âðð¡ ð â ð â²
ððð ðððð·ðµð = ðððð·ðµ ð â²}
ï§ cf. ðððð·ðµð = ðððð·ðµ ð â² â ðððð·ðµð = ðððð·ðµ ð â²
ï§ Xifeng Yan, Jiawei Han, Ramin Afshar, âCloSpan: Mining Closed Sequential Patterns in Large Datasetsâ, SIAM 2003
2019.11.18. 12
13. Pruning Redundant Pre-Conds
ï§ In a sequence database ðððð·ðµ, consider a pre-condition candidate ð ððð.
ï§ If there is a pre-condition candidate ð ððð
â²
â ð ððð such that
ï§ (i) ð ððð
â²
= ð1 ++ð ++ð2 while ð ððð = ð1 ++ð2, for some event ð and nonempty ð1, ð2
ï§ (ii) ðððð·ðµ ð ððð
= ðððð·ðµ ð ððð
â²
ï§ then, for any post-condition candidate ððð ð¡ and any forward extension ð ððð ++ð,
ï§ the rule ð ððð ++ð â ððð ð¡ is redundant
2019.11.18. 13
14. LS-Set BIDE
2019.11.18. 14
Backward-extension event checking is omitted from the original BIDE algorithm
⢠David Lo, Siau-Cheng KHOO, Chao LIU, âMining Recurrent Rules from Sequence Databaseâ, TR12/07 NUS
15. Non-Redundant Recurrent Rules Miner (NR3)
ï§ Input: a sequence database ðððð·ðµ; thresholds min_sup, min_supall, min_conf
ï§ Output: Significant and non-redundant recurrent rules ð ð¢ððð
ï§ Procedure
1. ðððð¶ððð â A pruned set of pre-conditions from ðððð·ðµ satisfying ððð _ð ð¢ð
2. foreach ððð â ðððð¶ððð do
1. ðððð·ðµððð
ððð â ðððð·ðµ allâprojected on ððð
2. ðð¡âð â ððð _ðððð à ðððð·ðµððð
ððð
3. ððð ð¡ð¶ððð â A pruned set of post-conditions from ðððð·ðµððð
ððð satisfying ðð¡âð
4. foreach ððð ð¡ â ððð ð¡ð¶ððð do
1. if ð ð¢ð ððð ððð ++ððð ð¡, ðððð·ðµ ⥠ððð _ð ð¢ð ððð then
1. ð ð¢ððð = ð ð¢ððð ⪠ððð â ððð ð¡
3. Remove remaining redundancy in ð ð¢ððð
ï§ Alias for Tasks
ï§ Procedure line 1 : GenPre task
ï§ Procedure line 2.1 â 2.4 : GenRule task
ï§ Procedure line 3 : RemRedun task
2019.11.18. 15
a c
b ac b
a a b c
ð
<a>â<c,a,d>
<a>â<c,b,b>
<a>â<b>
Rules
<a,b>â<c,d>
hash table <a>â<c,a,d>
<a>â<c,b,b>
<a,b>â<c,d>
<a,b>â<c,a>
<a>â<b>
Rules
<c,a,d>
28. Data Structure Level Optimization for Projections
ï§ For each sequence Si in SeqDB and a set I of events,
ï§ A hash map ðððð ⶠðŒ â 2 1,âŠ, ð ð
ï§ such that each key ð â ðŒ is mapped to the set of values each of which is a temporal point
of event e occurring in Si
2019.11.18. 28
29. Experiment Environment
ï§ Dataset
ï§ D10C10N10R0.5 (IBM synthetic data generator)
ï§ 9,678 sequences, average length 31.22
ï§ BMSWebView1 (a click stream dataset (Gazelle) from KDD Cup 2000)
ï§ 59,601 sequences, average length 2.42
ï§ Experiment Machine
ï§ Intel Core i7-3610QM 2.30GHz (4 physical and 8 logical cores)
ï§ 8GB RAM
ï§ Microsoft Windows 7 Professional x64
ï§ Implementation
ï§ Java SE 8
ï§ Default JVM settings
2019.11.18. 29
32. Discussion
ï§ Computational Complexity of the Algorithms
ï§ ðŒ ð à ðŒ ð (I : the set of events, k : the length of the longest frequent pattern)
ï§ The effects of fusing loops in NR3
ï§ The foreach loop in the GenRule step eliminated
ï§ The use of intermediate data ðððð·ðµððð simplifies the computation of
ï§ ðððð·ðµððð
ððð
= ðððð·ðµððð ⪠ðððð·ðµððð ððð ð¡ ððð
ððð
ï§ ð ð¢ð ððð
ððð â ððð ð¡, ðððð·ðµ = ð ð¢ð ððð
ððð ð¡, ðððð·ðµððð
ï§ The effect of the hash-based data structure
ï§ The efficient computation of (all-)projected databases
ï§ Using the hash-based data structure is not always efficient if the sequences are short
2019.11.18. 32
34. Loop-Fused NR3 (LF-NR3)
2019.11.18. â¹#âº
Possible to use the task-parallelism
underlying in the LF-NR3 algorithm,
⢠which can be handled within the
single-producer-multiple-consumer
framework
40. Additional Definitions
ï§ a sequence database ðððð·ðµ â a set of sequences
ï§ a sequence ð = ð1, ð2, ⊠, ð ð
ï§ the j-suffix of ð = ð ðâð+1, ð ðâð+2, ⊠, ð ð
ï§ ðâ² is the ð ð¡â minimum suffix of ð,
if ðâ²
is an suffix of ð iff no suffix starting with first(P) shorter than sx,
and longer than the (j-1)th minimum suffix
ï§ The ð ðð suf-projection of ðððð·ðµ with regarding to a pattern ð
ï§ ðððð·ðµð
ð ð¢ðâ ð
= ð, ð ð¥ |ðð = ðð¥ ++ð ð¥ â ðððð·ðµ, ð ð¥ is the ð ð¡â
minimum suffix of ðð of ð
ï§ ðððð·ðµ pre-projected on ð
ï§ ðððð·ðµð
ððð
= ð, ðð¥ ðð = ðð¥ ++ð ð¥ â ðððð·ðµ, ð ð¥ is ðð¡ð ðŠð¢ð§ð¢ðŠð®ðŠ ð¬ð®ððð¢ð± of ð }
2019.11.18. 40
41. Anti-Monotonicity Property of Confidence
ï§ Proposition 1
ï§ Consider a rule ð , in the form of ð ððð â ð ððð ð¡, and a sequence database ðððð·ðµ
ï§ ðððð ð , ðððð·ðµ =
sup ð ððð ð¡, ðððð·ðµ ð ððð
ððð
ð ð¢ð ððð ð ððð, ðððð·ðµ
=
ð ð¢ð ððð ð ððð, ðððð·ðµ ð ððð ð¡
ððð
ð ð¢ð ððð ð ððð, ðððð·ðµ
ï§ Proposition 2
ï§ Consider two rules ð and ð â² in a sequence database ðððð·ðµ with ð ððð
â² = ð ððð and
ð ððð ð¡
â²
= ð ++ð ððð ð¡ for some event ð â ðŒ
ï§ ðððð ð ⥠ðððð ð â²
ï§ Theorem. Anti-Monotonicity Property of Confidence
ï§ Consider two rules ð and ð â²
in a sequence database ðððð·ðµ with ð ððð
â²
= ð ððð and
ð ððð ð¡
â²
= ðð£ð ++ð ððð ð¡ where ðð£ð is an arbitrary series of events.
ï§ ðððð ð ⥠ðððð ð â²
ï§ If ð is not confident enough(ðððð ð < ððð_ðððð), ð â²
is not either
2019.11.18. 41
42. Pruning Redundant Post-Conds
ï§ In a sequence database ðððð·ðµ, consider a post condition candidate ð ððð ð¡.
ï§ Lemma 1
ï§ If there is a post-condition candidate ð ððð ð¡
â²
â ð ððð ð¡ such that
ï§ (i) ð ððð ð¡
â²
= ð1 ++ð ++ð2 while ð ððð ð¡ = ð1 ++ð2, for some event ð, subsequences ð1, (nonempty) ð2
ï§ (ii) ðððð·ðµ ð ððð ð¡
ððð
= ðððð·ðµ ð ððð ð¡
â²
ððð
ï§ then for any pre-condition candidate ððð and any backward extension ð ++ð ððð ð¡ of ð ððð ð¡, the rule ð =
ððð â ð ++ð ððð ð¡ is not confidence-closed
ï§ i.e., there exists another rule ð â²
â ð such that ðððð ð = ðððð ð â²
ï§ Lemma 2
ï§ If there is a post-condition candidate ð ððð ð¡
â²
â ð ððð ð¡ such that
ï§ (i) ð ððð ð¡
â²
= ð1 ++ð ++ð2 while ð ððð ð¡ = ð1 ++ð2, for some event ð, subsequences (nonempty) ð1, ð2
ï§ (iii) âð ⶠðððð·ðµ ð ððð ð¡
ð ð¢ðâð
= ðððð·ðµ ð ððð ð¡
â²
ð ð¢ðâð
, and
ï§ (iv) âð ⶠðððð·ðµ ð ððð ð¡
ð ð¢ðâð
ð ððð ð¡
ððð
= ðððð·ðµ ð ððð ð¡
â²
ð ð¢ðâð
ð ððð ð¡
â²
ððð
ï§ then for any pre-condition candidate ððð and any backward extension ð ++ð ððð ð¡ of ð ððð ð¡, the rule ð =
ððð â ð ++ð ððð ð¡ is not support-closed
ï§ i.e., there exists another rule ð â²
â ð such that ð ð¢ð ð = ð ð¢ð ð â²
and ð ð¢ð ððð
ð = ð ð¢ð ððð
ð â²
ï§ Theorem. Pruning Redundant Post-Conds
ï§ If the properties (i)-(iv) in Lemma 1 and 2 are satisfied,
ï§ then for any pre-condition candidate ððð and any backward extension ð ++ð ððð ð¡ of ð ððð ð¡, the rule ð =
ððð â ð ++ð ððð ð¡ is redundant.
2019.11.18. 42
45. Optimizing Operations
ï§ Given the sequence database ðððð·ðµ, and the rule ð = ððð â ððð ð¡
ï§ ð ð¢ð ð , ðððð·ðµ = ð ð¢ð ððð ð¡, ðððð·ðµððð
ï§ ð ð¢ð ððð ð , ðððð·ðµ = ð ð¢ð ððð ððð ð¡, ðððð·ðµððð
ï§ Pruning the search space of PRE early
ï§ for ð = ððð â ððð ð¡ and ð â² = ððð ++ð â ððð ð¡,
ï§ if ð ð¢ð ð , ðððð·ðµ †ððð_ð ð¢ð, then ð ð¢ð ð â², ðððð·ðµ †ððð_ð ð¢ð
ï§ if ð ð¢ð ððð
ð , ðððð·ðµ †ððð_ð ð¢ð ððð
, then ð ð¢ð ðð
ð â²
, ðððð·ðµ †ððð_ð ð¢ð ððð
ï§ Decreasing the number of scanning a database using a prefix tree
ï§ for each pre-condition ððð â ðð ðž, suppose that a node ð0 â ððððð has its children
nodes ð1, ⊠, ðð
ï§ we can compute the instance supports of its children nodes ð1, ⊠, ðð by scanning ðððð·ðµ
once
ï§ When ð0 corresponds to a post-condition ððð ð¡ â ðððð, each child node ðð corresponds to
a post-condition ððð ð¡ð = ðð ++ððð ð¡ for some event ðð, and the post condition of each child
node thus has its suffix ððð ð¡ in common.
ï§ When scanning a sequence ð â ðððð·ðµ, we record the positions of each ððâs and
those of the events appearing in ððð ð¡, from which we can compute the number of
instances of ððð ++ððð ð¡ð in ð
2019.11.18. â¹#âº
52. Conclusion & Future Works
ï§ Conclusion
ï§ We have proposed Parallel Non-Redundant Recurrent Rules Miner (pNR3)
ï§ We have proposed Loop-Fused Non-Redundant Recurrent Rules Miner(LF-NR3)
ï§ We have proposed Parallel Loop-Fused Non-Redundant Recurrent Rules Miner
(pLF-NR3)
ï§ We have proposed Interleaved Bidirectional Non-Redundant Recurrent Rules Miner
(iBiRM)
ï§ Future works
ï§ Improvement of the sequential recurrent rule mining algorithm
ï§ Improvement of the parallel algorithms
ï§ Source codes are available at https://bitbucket.org/sekilab/nr3
2019.11.18. 52
Editor's Notes
Good morning everyone.
I am Yoon SeungYong, a student in Nagoya Institute of Technology.
Seki Hirohisa is my advisor, and participated in this research.
From now, Iâd like to introduce my research, âParallel Mining of Non-Redundant Recurrent Rules from a Sequence Databaseâ.
I will, first, speak of the motivation of this research, and introduce the recurrent rules and the algorithm NR3, base of this research.
I, then, present our algorithm, parallel mining of recurrent rules, pNR3, and show the effectiveness of our algorithm based on experiment results.
Our motivation on the research
I first talk about the sequence database and sequential rules.
An example of a sequence database is transaction histories.
For instance, Alice rented Star Wars 4, 5, and 6, and then Star Wars 1, as the release date.
Another example is program traces.
From these databases, we can infer a rule <Star Wars 4> then <Star Wars 5>, and <lock> then <unlock>.
But why recurrent rules?
Because a recurrent rule captures temporal constraints within a sequence and across multiple sequences.
Recall the previous examples.
In the transaction histories, we rarely cares how many times a customer lend same videos.
But in the program traces, we have to consider how many times a series of commands has been executed.
This is the reason that a recurrent rule has been proposed
And mined recurrent rules can be directly converted into Linear Temporal Logic, the most widely used formalism for program verification.
For more details, refer a favorite text book, Model checking.
From now, I will introduce mining recurrent rules, and the algorithm NR3.
We first define some terminologies.
A sequence database is a set of sequences.
A sequence is a series of events.
In a sequence, we say the position of each event a temporal point.
And, we refer the first j event as the j-prefix of sequence.
We will define some operations on the sequence.
This is a concatenation of S and Sâ.
We say S is a super-sequence of Sâ, if S contains Sâ.
And the matched prefix is called as instance, and the shortest one is the minimum instance.
We will define the operation on a database.
We say a database is projected on a sequence P, if a sequence contains P, the longest remaining part will be a projected database, and as it is known operation.
We say a database is all-projected on a sequence P, if a sequence contains P, all of the remaining part will be a all-projected database.
We say the number of the sequences support, especially, the sequence support is for projection, and the instance support is for all-projection.
We will define a recurrent rule R equals pre then post.
The supports are almost same as we previously defined.
The confidence has special form, we can intuitively see it how many sequences contains post in the all-projected database on pre.
We say a rule is significant if the number of rules is above the thresholds.
We will define the notion of Rule Redundancy.
Consider these two rules.
R contains Râ, and have the same support and confidence.
It means if a sequence contains R then it also contains Râ.
We do not need to mine these rules, so we will prune some of them.
We define a rule is redundant if there is another longer rule that has the same support confidence.
And this will be processed using the algorithm BIDE, well-known frequent closed sequence miner.
Now I will introduce the algorithm of Non-Redundant Recurrent Rules Miner, NR3, the work of David Lo, and others.
The NR3 receives a sequence database and three thresholds, and emits significant and non-redundant recurrent rules.
It first generates the candidates of pre-conditions using BIDE, consisting of recursions.
So we call this step GenPre.
Next, by looping the candidate pre, it generates the candidates of post-conditions and generates rules.
We call this step GenRule, and in this step, we get significant rules.
Finally, we remove remaining redundant rules using hash tables using the supports and confidence as a key.
We call this step RemRedun.
From now I will show our algorithm, parallel mining of recurrent rules, pNR3.
Letâs review the previous work.
First, if GenPre task find one pre-condition candidate, then we can handle GenRule task immediately.
We call this strategy, the single-producer-multiple-consumer-framework.
Because the GenRule tasks can be consumed as the GenPre task produces a pre.
Second, we can concurrently handle the GenRule tasks.
We call this strategy, namely, the loop-level parallelization.
This is our algorithm Parallel Non-Redundant Recurrent Rules Miner, pNR3.
The pNR3 instance starts to mine pre-conditions.
Then the GenPre emits GenRule tasks using found pre, and push them into the thread pool.
The thread pool handles these GenRule tasks, and the tasks collect significant rules.
Finally the RemRedun instance removes redundant rules.
This is our Java implementation.
It works as I explained.
The source codes are available at our Bitbucket repository.
I will discuss the effect of parallelization.
We utilized two strategy, GenPre Concurrency, the single-producer-multiple-consumer framework and GenRule Parallelization, the loop-level parallelization.
GenPre Concurrency works as maximum function of GenPre or GenRule, because the longer task effects the total runtime.
GenRule Parallelization works as a divider function, because available threads can handle each GenRule task.
As a result, the runtime of our pNR3 is max GenPre or GenRule divided by N plus RemRedun.
We will see these discussion in experiment results.
Iâll explain experiment environment.
We used two famous dataset, one is a synthetic dataset and another is real dataset.
We implemented nr3 and pNR3 in Java 8, and executed in the common Core i7 machine which has 4 physical cores.
This is an experiment result on synthetic dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenPre takes about 20% of runtime, and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm becomes 20% of this dataset, then we can say our algorithm is effective.
As the results show, the runtime of 8-pNR3 is about 20% of NR3, so we can say our algorithm is very effective.
This is an experiment result on real world dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenRule takes almost 100% of runtime, and GenPre and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm decreases as we increase the number of threads, then we can say our algorithm is effective.
As the results show, the runtime of 4-pNR3 is about 30% of NR3, and 8-pNR3 is about 20% of NR3, so we can say our algorithm is effective, even if we take account into some overheads due to parallelization.
From now I will show our algorithm, parallel mining of recurrent rules, pNR3.
Now I will introduce the algorithm of Non-Redundant Recurrent Rules Miner, NR3, the work of David Lo, and others.
The NR3 receives a sequence database and three thresholds, and emits significant and non-redundant recurrent rules.
It first generates the candidates of pre-conditions using BIDE, consisting of recursions.
So we call this step GenPre.
Next, by looping the candidate pre, it generates the candidates of post-conditions and generates rules.
We call this step GenRule, and in this step, we get significant rules.
Finally, we remove remaining redundant rules using hash tables using the supports and confidence as a key.
We call this step RemRedun.
Iâll explain experiment environment.
We used two famous dataset, one is a synthetic dataset and another is real dataset.
We implemented nr3 and pNR3 in Java 8, and executed in the common Core i7 machine which has 4 physical cores.
This is an experiment result on synthetic dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenPre takes about 20% of runtime, and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm becomes 20% of this dataset, then we can say our algorithm is effective.
As the results show, the runtime of 8-pNR3 is about 20% of NR3, so we can say our algorithm is very effective.
This is an experiment result on real world dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenRule takes almost 100% of runtime, and GenPre and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm decreases as we increase the number of threads, then we can say our algorithm is effective.
As the results show, the runtime of 4-pNR3 is about 30% of NR3, and 8-pNR3 is about 20% of NR3, so we can say our algorithm is effective, even if we take account into some overheads due to parallelization.
From now I will show our algorithm, parallel mining of recurrent rules, pNR3.
Iâll explain experiment environment.
We used two famous dataset, one is a synthetic dataset and another is real dataset.
We implemented nr3 and pNR3 in Java 8, and executed in the common Core i7 machine which has 4 physical cores.
This is an experiment result on synthetic dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenPre takes about 20% of runtime, and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm becomes 20% of this dataset, then we can say our algorithm is effective.
As the results show, the runtime of 8-pNR3 is about 20% of NR3, so we can say our algorithm is very effective.
This is an experiment result on real world dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenRule takes almost 100% of runtime, and GenPre and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm decreases as we increase the number of threads, then we can say our algorithm is effective.
As the results show, the runtime of 4-pNR3 is about 30% of NR3, and 8-pNR3 is about 20% of NR3, so we can say our algorithm is effective, even if we take account into some overheads due to parallelization.
From now, I will introduce mining recurrent rules, and the algorithm NR3.
We first define some terminologies.
A sequence database is a set of sequences.
A sequence is a series of events.
In a sequence, we say the position of each event a temporal point.
And, we refer the first j event as the j-prefix of sequence.
From now I will show our algorithm, parallel mining of recurrent rules, pNR3.
Iâll explain experiment environment.
We used two famous dataset, one is a synthetic dataset and another is real dataset.
We implemented nr3 and pNR3 in Java 8, and executed in the common Core i7 machine which has 4 physical cores.
This is an experiment result on synthetic dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenPre takes about 20% of runtime, and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm becomes 20% of this dataset, then we can say our algorithm is effective.
As the results show, the runtime of 8-pNR3 is about 20% of NR3, so we can say our algorithm is very effective.
This is an experiment result on real world dataset.
Above is when change minimum support, and below is when change confidence.
First chart is a runtime of algorithms, NR3 and pNR3 on 2, 4, 8 threads, second is the ratio of each tasks in NR3, and third is the size of pre-condition candidates and rules.
As we discussed before, the runtime of our parallel algorithm is maximum of GenPre and GenRule divided by N plus RemRedun.
In NR3, GenRule takes almost 100% of runtime, and GenPre and RemRedun is negligible in this dataset.
So if the runtime of our parallel algorithm decreases as we increase the number of threads, then we can say our algorithm is effective.
As the results show, the runtime of 4-pNR3 is about 30% of NR3, and 8-pNR3 is about 20% of NR3, so we can say our algorithm is effective, even if we take account into some overheads due to parallelization.
Now I finally conclude
We have proposed the algorithm Parallel Non-Redundant Recurrent Rules Miner, pNR3.
It utilized two strategy, the single-producer-multiple-consumer framework and the loop-level parallelism.
We showed the effectiveness of our algorithm based on the experiment on synthetic and real datasets.
For the future works, we will do some experiments on the program trace, as the purpose of the rules.
We will do experiment on many cores processor to see the effects accurately.
Also, using the large memory, we will compare our algorithm to BOB, the successor of NR3.
We are now working on improvement of the sequential recurrent rule mining algorithms.
You can refer our implementation in this repository.
This is all of my presentation.
Thank you for listening.
Do you have any questions?