This paper proposed a novel tree structure, DSTree, which can handle the stream data. The experiments show the comparable performance in terms of accuracy and efficiency.
This document provides an overview of scalable pattern mining algorithms for large scale interval data. It discusses the need for scalable pattern mining due to the huge increase in data size. It covers serial frequent itemset mining methods like Apriori, Eclat, and FP-growth. It also discusses parallel itemset mining methods including FP-growth based PFP algorithm and ultrametric tree based FiDoop algorithm. Additionally, it covers pattern mining approaches for interval data, including interval sequences, temporal relations, and hierarchical representations. The document concludes by stating that while efforts have been made to modify classic algorithms for distributed processing, scalable mining of temporal relationships on large interval data remains an open issue.
This document discusses soft clustering techniques for fraud and credit risk modeling. It begins by explaining the need for account segmentation in risk modeling, as accounts exhibit different behaviors. K-means clustering is commonly used but has limitations. The document then introduces several soft clustering methods - fuzzy c-means clustering, fuzzy c-means with extragrades, possibilistic clustering, and kernel-based clustering - that allow accounts to belong to multiple clusters. It provides examples applying these techniques to an iris data set and recommends when each soft clustering method is most appropriate. The document concludes by describing how to use the soft clustering results in model building and scoring phases.
DLmalloc is a memory allocator that maintains free memory chunks in bins to avoid system calls. It uses various structures like chunks, bins, and a management table. For small requests, it first checks bins and the DV chunk, then may split chunks or use the top chunk. For large requests, it uses the smallest fitting binned chunk or DV chunk, then calls system functions if needed. Realloc tries to extend or copy as needed. Free merges chunks if possible and inserts them in bins, skipping top and DV. Trim unmaps excess space from the top chunk and free mmap segments.
Introduction to Data streaming - 05/12/2014Raja Chiky
Raja Chiky is an associate professor whose research interests include data stream mining, distributed architectures, and recommender systems. The document outlines data streaming concepts including what a data stream is, data stream management systems, and basic approximate algorithms used for processing massive, high-velocity data streams. It also discusses challenges in distributed systems and using semantic technologies for data streaming.
The study on mining temporal patterns and related applications in dynamic soc...Thanh Hieu
The document provides a curriculum vitae for Yi-Cheng Chen that includes basic information, education history, and research interests. It notes that Chen received a B.S. from Yuan Ze University in 2000, an M.S. from National Taiwan University of Science and Technology in 2002, and a Ph.D. from National Chiao Tung University in 2012 under the advisement of Professors Suh-Yin Lee and Wen-Chih Peng. Chen's Ph.D. dissertation focused on time interval-based sequential pattern mining. The CV outlines Chen's current research interests as temporal pattern mining, social network analysis, smart home applications, and cloud computing.
The document discusses Dremel, an interactive query system for analyzing large-scale datasets. Dremel uses a columnar data storage format and a multi-level query execution tree to enable fast querying. It evaluates Dremel's performance on interactive queries, showing it can count terms in a field within seconds using 3000 workers, while MapReduce takes hours. Dremel also scales linearly and handles stragglers well. Today, similar systems like Google BigQuery and Apache Drill use Dremel-like techniques for interactive analysis of web-scale data.
Dremel: Interactive Analysis of Web-Scale Datasets robertlz
The document describes Dremel, an interactive analysis system for web-scale datasets. Dremel uses a columnar data storage model and tree-based query serving architecture to enable interactive analysis of trillion record datasets distributed across thousands of nodes. It provides an SQL-like interface and can process queries orders of magnitude faster than traditional MapReduce systems by avoiding record assembly costs. Experiments show Dremel can analyze tens to hundreds of billions of records interactively on commodity hardware.
Dremel interactive analysis of web scale datasetsCarl Lu
Dremel is an interactive query system that can analyze large web-scale datasets containing trillions of records in seconds. It uses a columnar data layout and multi-level query execution trees to distribute queries across thousands of servers. Dremel's nested data model and column-striped storage allows it to efficiently retrieve and analyze only the necessary columns from large datasets. Experimental results demonstrated Dremel's ability to process queries over datasets containing trillions of records and petabytes of data in seconds using a cluster of thousands of servers.
This document provides an overview of scalable pattern mining algorithms for large scale interval data. It discusses the need for scalable pattern mining due to the huge increase in data size. It covers serial frequent itemset mining methods like Apriori, Eclat, and FP-growth. It also discusses parallel itemset mining methods including FP-growth based PFP algorithm and ultrametric tree based FiDoop algorithm. Additionally, it covers pattern mining approaches for interval data, including interval sequences, temporal relations, and hierarchical representations. The document concludes by stating that while efforts have been made to modify classic algorithms for distributed processing, scalable mining of temporal relationships on large interval data remains an open issue.
This document discusses soft clustering techniques for fraud and credit risk modeling. It begins by explaining the need for account segmentation in risk modeling, as accounts exhibit different behaviors. K-means clustering is commonly used but has limitations. The document then introduces several soft clustering methods - fuzzy c-means clustering, fuzzy c-means with extragrades, possibilistic clustering, and kernel-based clustering - that allow accounts to belong to multiple clusters. It provides examples applying these techniques to an iris data set and recommends when each soft clustering method is most appropriate. The document concludes by describing how to use the soft clustering results in model building and scoring phases.
DLmalloc is a memory allocator that maintains free memory chunks in bins to avoid system calls. It uses various structures like chunks, bins, and a management table. For small requests, it first checks bins and the DV chunk, then may split chunks or use the top chunk. For large requests, it uses the smallest fitting binned chunk or DV chunk, then calls system functions if needed. Realloc tries to extend or copy as needed. Free merges chunks if possible and inserts them in bins, skipping top and DV. Trim unmaps excess space from the top chunk and free mmap segments.
Introduction to Data streaming - 05/12/2014Raja Chiky
Raja Chiky is an associate professor whose research interests include data stream mining, distributed architectures, and recommender systems. The document outlines data streaming concepts including what a data stream is, data stream management systems, and basic approximate algorithms used for processing massive, high-velocity data streams. It also discusses challenges in distributed systems and using semantic technologies for data streaming.
The study on mining temporal patterns and related applications in dynamic soc...Thanh Hieu
The document provides a curriculum vitae for Yi-Cheng Chen that includes basic information, education history, and research interests. It notes that Chen received a B.S. from Yuan Ze University in 2000, an M.S. from National Taiwan University of Science and Technology in 2002, and a Ph.D. from National Chiao Tung University in 2012 under the advisement of Professors Suh-Yin Lee and Wen-Chih Peng. Chen's Ph.D. dissertation focused on time interval-based sequential pattern mining. The CV outlines Chen's current research interests as temporal pattern mining, social network analysis, smart home applications, and cloud computing.
The document discusses Dremel, an interactive query system for analyzing large-scale datasets. Dremel uses a columnar data storage format and a multi-level query execution tree to enable fast querying. It evaluates Dremel's performance on interactive queries, showing it can count terms in a field within seconds using 3000 workers, while MapReduce takes hours. Dremel also scales linearly and handles stragglers well. Today, similar systems like Google BigQuery and Apache Drill use Dremel-like techniques for interactive analysis of web-scale data.
Dremel: Interactive Analysis of Web-Scale Datasets robertlz
The document describes Dremel, an interactive analysis system for web-scale datasets. Dremel uses a columnar data storage model and tree-based query serving architecture to enable interactive analysis of trillion record datasets distributed across thousands of nodes. It provides an SQL-like interface and can process queries orders of magnitude faster than traditional MapReduce systems by avoiding record assembly costs. Experiments show Dremel can analyze tens to hundreds of billions of records interactively on commodity hardware.
Dremel interactive analysis of web scale datasetsCarl Lu
Dremel is an interactive query system that can analyze large web-scale datasets containing trillions of records in seconds. It uses a columnar data layout and multi-level query execution trees to distribute queries across thousands of servers. Dremel's nested data model and column-striped storage allows it to efficiently retrieve and analyze only the necessary columns from large datasets. Experimental results demonstrated Dremel's ability to process queries over datasets containing trillions of records and petabytes of data in seconds using a cluster of thousands of servers.
1) The document provides an overview of the steps to conduct DTI analysis using FSL and TBSS. This includes organizing data, preprocessing such as eddy current correction and tensor fitting, and using TBSS for voxelwise statistical analysis.
2) Key preprocessing steps include extracting the b0 image, generating a brain mask from b0, eddy current correction, and tensor fitting. For TBSS, preprocessing involves nonlinear registration of FA images to a standard space, thresholding the mean FA skeleton, and projecting data onto the skeleton.
3) TBSS can also be conducted on non-FA images like MD or MO by registering these images to the precomputed FA alignment using tbss_non_FA.
Large scale data-parsing with Hadoop in BioinformaticsNtino Krampis
This document discusses using Hadoop and MapReduce to perform large-scale data parsing and algorithm development. It provides examples of finding members of protein clusters in a dataset containing 12 million rows and 30GB of data. Traditional approaches like hashing and sorting the data are discussed and compared to the MapReduce approach. The MapReduce approach automatically handles data distribution across nodes, parallel processing of data fragments using Map and Reduce functions, and task scheduling to handle failures. Key aspects of MapReduce like the Map, Shuffle, and Reduce phases are outlined.
OS-assisted task preemption allows Hadoop tasks to be suspended by the operating system instead of killed when preempted. This approach mirrors how the OS already suspends processes using signals. When memory runs low, suspended tasks are paged out efficiently by the OS. Experiments show that suspending tasks provides better response times for high priority tasks than waiting or killing low priority tasks. Some considerations for implementing suspension-friendly tasks are controlling memory usage and handling external state changes during suspension.
This set of slides is based on the presentation I gave at ACM DataScience camp 2014. This is suitable for those who are still new to R. It has a few basic data manipulation techniques, and then goes into the basics of using of the dplyr package (Hadley Wickham) #rstats #dplyr
Frequency-based Constraint Relaxation for Private Query Processing in Cloud D...Junpei Kawamoto
This document proposes a frequency-based constraint relaxation methodology for private queries in cloud databases. It aims to reduce computational costs for servers while maintaining privacy risks below existing "complete" protocols. The approach relaxes the constraint that servers must check all database items for a query by instead checking a subset, or "handled set", based on search intention frequencies. Evaluation on a real dataset found the approach reduces average query costs to 6.5% of complete protocols while keeping privacy risks comparable.
The document discusses MapReduce model, including its computing model, basic architecture, HDFS, MapReduce and cluster deployment. It provides code examples of Mapper, Reducer and main functions in MapReduce job. It also talks about hardware selection, operating system choice, kernel tuning, disk configuration, network setup and Hadoop environment configuration for cluster deployment.
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
This document provides an overview of the dplyr package in R. It describes several key functions in dplyr for manipulating data frames, including verbs like filter(), select(), arrange(), mutate(), and summarise(). It also covers grouping data with group_by() and joining data with joins like inner_join(). Pipelines of dplyr operations can be chained together using the %>% operator from the magrittr package. The document concludes that dplyr provides simple yet powerful verbs for transforming data frames in a convenient way.
In this presentation we’ll look at five ways in which we can use efficient coding to help our garbage collector spend less CPU time allocating and freeing memory, and reduce GC overhead.
This document discusses optimization techniques for memory and cache usage. It begins with an overview of the memory hierarchy and justification for optimization. It then covers optimizing code and data caches through techniques like prefetching, structure layout, tree data structures, and linearization caching. It also discusses memory allocation policies and reducing aliasing through techniques like restricting pointers and analysis. The overall goal is to discuss how to improve cache utilization and thereby increase performance.
This document discusses strategies for analyzing moderately large data sets in R when the total number of observations (N) times the total number of variables (P) is too large to fit into memory all at once. It presents several approaches including loading data incrementally from files or databases, using randomized algorithms, and outsourcing computations to SQL. Specific examples discussed include linear regression on large data sets and whole genome association studies.
An improved Item-based Maxcover Algorithm to protect Sensitive Patterns in La...IOSR Journals
This document presents an improved item-based maxcover algorithm to protect sensitive patterns in large databases. The algorithm aims to minimize information loss when sanitizing databases to hide sensitive patterns. It works by identifying sensitive transactions containing restrictive patterns. It then sorts these transactions by degree and size and selects victim items to remove based on which items have the maximum cover across multiple patterns. This is done with only one scan of the source database. Experimental results on real datasets show the algorithm achieves zero hiding failure and low misses costs between 0-2.43% while keeping the sanitization rate between 40-68% and information loss below 1.1%.
The document defines a function called covcor() that calculates and returns the covariance and correlation between variables in a data frame. The function takes a data frame as input, splits it by a grouping variable, applies covariance and correlation calculations to subsets of the data, and combines the results into an output data frame. Three methods for defining the covcor() function are presented: 1) Using subset() and merge(), 2) Using tapply(), and 3) Using ddply() from the plyr package. The function is demonstrated on orange tree data to calculate covariance and correlation between tree age and circumference for each tree. Transforming the circumference variable affects the covariance but not the correlation, demonstrating properties of these statistical measures.
Talk given at Minds Mastering Machines #M3London October 2018. Many AI projects are plagued with inefficiency since we have prioritised speed of development over pragmatic development. There are many areas that we can improve and here is a selection of basic changes that all AI practitioners should understand. Please see the speaker notes for more information and references.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
An Efficient Algorithm for Mining Frequent Itemsets within Large Windows over...Waqas Tariq
Sliding window is an interesting model for frequent pattern mining over data stream due to handling concept change by considering recent data. In this study, a novel approximate algorithm for frequent itemset mining is proposed which operates in both transactional and time sensitive sliding window model. This algorithm divides the current window into a set of partitions and estimates the support of newly appeared itemsets within the previous partitions of the window. By monitoring essential set of itemsets within incoming data, this algorithm does not waste processing power for itemsets which are not frequent in the current window. Experimental evaluations using both synthetic and real datasets shows the superiority of the proposed algorithm with respect to previously proposed algorithms.
Mining Maximum Frequent Item Sets Over Data Streams Using Transaction Sliding...ijitcs
As we know that the online mining of streaming data is one of the most important issues in data mining. In
this paper, we proposed an efficient one- .frequent item sets over a transaction-sensitive sliding window),
to mine the set of all frequent item sets in data streams with a transaction-sensitive sliding window. An
effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and
memory needed to slide the windows. The experiments show that the proposed algorithm not only attain
highly accurate mining results, but also the performance significant faster and consume less memory than
existing algorithms for mining frequent item sets over recent data streams. In this paper our theoretical
analysis and experimental studies show that the proposed algorithm is efficient and scalable and perform
better for mining the set of all maximum frequent item sets over the entire history of the data streams.
FREQUENT ITEMSET MINING IN TRANSACTIONAL DATA STREAMS BASED ON QUALITY CONTRO...IJDKP
The document describes a proposed algorithm called RAQ-FIG for mining frequent itemsets from transactional data streams. It operates using a sliding window model composed of basic windows. The algorithm has three phases: 1) initializing the sliding window by filling it with recent transactions from a buffer, 2) generating bit sequences for each basic window and finding frequent itemsets through bitwise operations, and 3) adapting the algorithm's processing based on available memory and quality metrics to ensure efficient resource usage and accurate results. The algorithm aims to account for computational resources and dynamically adjust the processing rate based on available memory while computing recent approximate frequent itemsets with a single pass.
The challenges with respect to mining frequent items over data streaming engaging variable window size
and low memory space are addressed in this research paper. To check the varying point of context change
in streaming transaction we have developed a window structure which will be in two levels and supports in
fixing the window size instantly and controls the heterogeneities and assures homogeneities among
transactions added to the window. To minimize the memory utilization, computational cost and improve the
process scalability, this design will allow fixing the coverage or support at window level. Here in this
document, an incremental mining of frequent item-sets from the window and a context variation analysis
approach are being introduced. The complete technology that we are presenting in this document is named
as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWSCVA).
There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this
design we have used window size change to represent the conceptual drift in an information stream. As it
were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The
experiments that we have executed and documented proved that the algorithm that we have designed is
much efficient than that of existing.
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
1) The document provides an overview of the steps to conduct DTI analysis using FSL and TBSS. This includes organizing data, preprocessing such as eddy current correction and tensor fitting, and using TBSS for voxelwise statistical analysis.
2) Key preprocessing steps include extracting the b0 image, generating a brain mask from b0, eddy current correction, and tensor fitting. For TBSS, preprocessing involves nonlinear registration of FA images to a standard space, thresholding the mean FA skeleton, and projecting data onto the skeleton.
3) TBSS can also be conducted on non-FA images like MD or MO by registering these images to the precomputed FA alignment using tbss_non_FA.
Large scale data-parsing with Hadoop in BioinformaticsNtino Krampis
This document discusses using Hadoop and MapReduce to perform large-scale data parsing and algorithm development. It provides examples of finding members of protein clusters in a dataset containing 12 million rows and 30GB of data. Traditional approaches like hashing and sorting the data are discussed and compared to the MapReduce approach. The MapReduce approach automatically handles data distribution across nodes, parallel processing of data fragments using Map and Reduce functions, and task scheduling to handle failures. Key aspects of MapReduce like the Map, Shuffle, and Reduce phases are outlined.
OS-assisted task preemption allows Hadoop tasks to be suspended by the operating system instead of killed when preempted. This approach mirrors how the OS already suspends processes using signals. When memory runs low, suspended tasks are paged out efficiently by the OS. Experiments show that suspending tasks provides better response times for high priority tasks than waiting or killing low priority tasks. Some considerations for implementing suspension-friendly tasks are controlling memory usage and handling external state changes during suspension.
This set of slides is based on the presentation I gave at ACM DataScience camp 2014. This is suitable for those who are still new to R. It has a few basic data manipulation techniques, and then goes into the basics of using of the dplyr package (Hadley Wickham) #rstats #dplyr
Frequency-based Constraint Relaxation for Private Query Processing in Cloud D...Junpei Kawamoto
This document proposes a frequency-based constraint relaxation methodology for private queries in cloud databases. It aims to reduce computational costs for servers while maintaining privacy risks below existing "complete" protocols. The approach relaxes the constraint that servers must check all database items for a query by instead checking a subset, or "handled set", based on search intention frequencies. Evaluation on a real dataset found the approach reduces average query costs to 6.5% of complete protocols while keeping privacy risks comparable.
The document discusses MapReduce model, including its computing model, basic architecture, HDFS, MapReduce and cluster deployment. It provides code examples of Mapper, Reducer and main functions in MapReduce job. It also talks about hardware selection, operating system choice, kernel tuning, disk configuration, network setup and Hadoop environment configuration for cluster deployment.
Scott Bailey
Few things we model in our databases are as complicated as time. The major database vendors have struggled for years with implementing the base data types to represent time. And the capabilities and functionality vary wildly among databases. Fortunately PostgreSQL has one of the best implementations out there. We will look at PostgreSQL's core functionality, discuss temporal extensions, modeling temporal data, time travel and bitemporal data.
This document provides an overview of the dplyr package in R. It describes several key functions in dplyr for manipulating data frames, including verbs like filter(), select(), arrange(), mutate(), and summarise(). It also covers grouping data with group_by() and joining data with joins like inner_join(). Pipelines of dplyr operations can be chained together using the %>% operator from the magrittr package. The document concludes that dplyr provides simple yet powerful verbs for transforming data frames in a convenient way.
In this presentation we’ll look at five ways in which we can use efficient coding to help our garbage collector spend less CPU time allocating and freeing memory, and reduce GC overhead.
This document discusses optimization techniques for memory and cache usage. It begins with an overview of the memory hierarchy and justification for optimization. It then covers optimizing code and data caches through techniques like prefetching, structure layout, tree data structures, and linearization caching. It also discusses memory allocation policies and reducing aliasing through techniques like restricting pointers and analysis. The overall goal is to discuss how to improve cache utilization and thereby increase performance.
This document discusses strategies for analyzing moderately large data sets in R when the total number of observations (N) times the total number of variables (P) is too large to fit into memory all at once. It presents several approaches including loading data incrementally from files or databases, using randomized algorithms, and outsourcing computations to SQL. Specific examples discussed include linear regression on large data sets and whole genome association studies.
An improved Item-based Maxcover Algorithm to protect Sensitive Patterns in La...IOSR Journals
This document presents an improved item-based maxcover algorithm to protect sensitive patterns in large databases. The algorithm aims to minimize information loss when sanitizing databases to hide sensitive patterns. It works by identifying sensitive transactions containing restrictive patterns. It then sorts these transactions by degree and size and selects victim items to remove based on which items have the maximum cover across multiple patterns. This is done with only one scan of the source database. Experimental results on real datasets show the algorithm achieves zero hiding failure and low misses costs between 0-2.43% while keeping the sanitization rate between 40-68% and information loss below 1.1%.
The document defines a function called covcor() that calculates and returns the covariance and correlation between variables in a data frame. The function takes a data frame as input, splits it by a grouping variable, applies covariance and correlation calculations to subsets of the data, and combines the results into an output data frame. Three methods for defining the covcor() function are presented: 1) Using subset() and merge(), 2) Using tapply(), and 3) Using ddply() from the plyr package. The function is demonstrated on orange tree data to calculate covariance and correlation between tree age and circumference for each tree. Transforming the circumference variable affects the covariance but not the correlation, demonstrating properties of these statistical measures.
Talk given at Minds Mastering Machines #M3London October 2018. Many AI projects are plagued with inefficiency since we have prioritised speed of development over pragmatic development. There are many areas that we can improve and here is a selection of basic changes that all AI practitioners should understand. Please see the speaker notes for more information and references.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
An Efficient Algorithm for Mining Frequent Itemsets within Large Windows over...Waqas Tariq
Sliding window is an interesting model for frequent pattern mining over data stream due to handling concept change by considering recent data. In this study, a novel approximate algorithm for frequent itemset mining is proposed which operates in both transactional and time sensitive sliding window model. This algorithm divides the current window into a set of partitions and estimates the support of newly appeared itemsets within the previous partitions of the window. By monitoring essential set of itemsets within incoming data, this algorithm does not waste processing power for itemsets which are not frequent in the current window. Experimental evaluations using both synthetic and real datasets shows the superiority of the proposed algorithm with respect to previously proposed algorithms.
Mining Maximum Frequent Item Sets Over Data Streams Using Transaction Sliding...ijitcs
As we know that the online mining of streaming data is one of the most important issues in data mining. In
this paper, we proposed an efficient one- .frequent item sets over a transaction-sensitive sliding window),
to mine the set of all frequent item sets in data streams with a transaction-sensitive sliding window. An
effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and
memory needed to slide the windows. The experiments show that the proposed algorithm not only attain
highly accurate mining results, but also the performance significant faster and consume less memory than
existing algorithms for mining frequent item sets over recent data streams. In this paper our theoretical
analysis and experimental studies show that the proposed algorithm is efficient and scalable and perform
better for mining the set of all maximum frequent item sets over the entire history of the data streams.
FREQUENT ITEMSET MINING IN TRANSACTIONAL DATA STREAMS BASED ON QUALITY CONTRO...IJDKP
The document describes a proposed algorithm called RAQ-FIG for mining frequent itemsets from transactional data streams. It operates using a sliding window model composed of basic windows. The algorithm has three phases: 1) initializing the sliding window by filling it with recent transactions from a buffer, 2) generating bit sequences for each basic window and finding frequent itemsets through bitwise operations, and 3) adapting the algorithm's processing based on available memory and quality metrics to ensure efficient resource usage and accurate results. The algorithm aims to account for computational resources and dynamically adjust the processing rate based on available memory while computing recent approximate frequent itemsets with a single pass.
The challenges with respect to mining frequent items over data streaming engaging variable window size
and low memory space are addressed in this research paper. To check the varying point of context change
in streaming transaction we have developed a window structure which will be in two levels and supports in
fixing the window size instantly and controls the heterogeneities and assures homogeneities among
transactions added to the window. To minimize the memory utilization, computational cost and improve the
process scalability, this design will allow fixing the coverage or support at window level. Here in this
document, an incremental mining of frequent item-sets from the window and a context variation analysis
approach are being introduced. The complete technology that we are presenting in this document is named
as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWSCVA).
There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this
design we have used window size change to represent the conceptual drift in an information stream. As it
were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The
experiments that we have executed and documented proved that the algorithm that we have designed is
much efficient than that of existing.
Simulation and Performance Analysis of Long Term Evolution (LTE) Cellular Net...ijsrd.com
In the development, standardization and implementation of LTE Networks based on Orthogonal Freq. Division Multiple Access (OFDMA), simulations are necessary to test as well as optimize algorithms and procedures before real time establishment. This can be done by both Physical Layer (Link-Level) and Network (System-Level) context. This paper proposes Network Simulator 3 (NS-3) which is capable of evaluating the performance of the Downlink Shared Channel of LTE networks and comparing it with available MatLab based LTE System Level Simulator performance.
Evaluating Classification Algorithms Applied To Data Streams Esteban DonatoEsteban Donato
This document summarizes and evaluates several algorithms for classification of data streams: VFDTc, UFFT, and CVFDT. It describes their approaches for handling concept drift, detecting outliers and noise. The algorithms were tested on synthetic data streams generated with configurable attributes like drift frequency and noise percentage. Results show VFDTc and UFFT performed best in accuracy, while CVFDT and UFFT were fastest. The study aims to help choose algorithms suitable for different data stream characteristics like gradual vs sudden drift or frequent vs infrequent drift.
FP-Tree is also a huge hierarchical data structure and cannot fit into the main memory also it is not suitable for “Incremental-mining” nor used in “Interactive-mining” system
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a new approach called CBSW (Chernoff Bound based Sliding Window) for mining frequent itemsets from data streams. CBSW uses concepts from the Chernoff bound to dynamically determine the window size for mining frequent itemsets. It monitors boundary movements in a synopsis data structure to detect changes in the data stream and adjusts the window size accordingly. Experimental results demonstrate the effectiveness of CBSW in mining frequent itemsets from high-speed data streams.
The document summarizes a presentation about mining top-k frequent closed itemsets over data streams using a sliding window model. It introduces the challenges of data stream mining and focuses on mining frequent closed itemsets. It proposes an efficient single-pass algorithm called FCI_max that discovers the top-k frequent closed itemsets of length no more than a maximum length using a sliding window technique, without specifying a minimum support. An example is provided to illustrate how FCI_max works on a sample data stream over 4 time windows of 5 minutes each.
A Survey on Improve Efficiency And Scability vertical mining using Agriculter...Editor IJMTER
Basic idea is that the search tree could be divided into sub process of equivalence
classes. And since generating item sets in sub process of equivalence classes is independent from
each other, we could do frequent item set mining in sub trees of equivalence classes in parallel. So
the straightforward approach to parallelize Éclat is to consider each equivalence class as a data
(agriculture). We can distribute data to different nodes and nodes could work on data without any
synchronization. Even though the sorting helps to produce different sets in smaller sizes, there is a
cost for sorting. Our Research to analysis is that the size of equivalence class is relatively small
(always less than the size of the item base) and this size also reduces quickly as the search goes
deeper in the recursion process. Base on time using more than using agriculture data we can handle
large amount of data so first we develop éclat algorithm then develop parallel éclat algorithm then
compare with using same data with respect time .with the help of support and confidence.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
This document discusses an approach for mining frequent itemsets from data streams using the Chernoff bound and sliding window model. The proposed CB-based method approximates itemset counts from summary information without rescanning the stream, making it adaptive to streams with different distributions. Experiments showed the method performs better in optimizing memory usage and mining recent patterns in less time with accurate results. The document reviews related work on frequent itemset mining from data streams and motivates the need for an efficient model to handle time-sensitive items in uncertain streams.
A New Data Stream Mining Algorithm for Interestingness-rich Association RulesVenu Madhav
Frequent itemset mining and association rule generation is
a challenging task in data stream. Even though, various algorithms
have been proposed to solve the issue, it has been found
out that only frequency does not decides the significance
interestingness of the mined itemset and hence the association
rules. This accelerates the algorithms to mine the association
rules based on utility i.e. proficiency of the mined rules. However,
fewer algorithms exist in the literature to deal with the utility
as most of them deals with reducing the complexity in frequent
itemset/association rules mining algorithm. Also, those few
algorithms consider only the overall utility of the association
rules and not the consistency of the rules throughout a defined
number of periods. To solve this issue, in this paper, an enhanced
association rule mining algorithm is proposed. The algorithm
introduces new weightage validation in the conventional
association rule mining algorithms to validate the utility and
its consistency in the mined association rules. The utility is
validated by the integrated calculation of the cost/price efficiency
of the itemsets and its frequency. The consistency validation
is performed at every defined number of windows using the
probability distribution function, assuming that the weights are
normally distributed. Hence, validated and the obtained rules
are frequent and utility efficient and their interestingness are
distributed throughout the entire time period. The algorithm is
implemented and the resultant rules are compared against the
rules that can be obtained from conventional mining algorithms
This document summarizes a research paper that proposes a new resource scheduling algorithm called STRS for cloud computing environments. STRS aims to optimally allocate data resources across computational clusters in a distributed system to minimize data access costs. It does this through two distributed algorithms - STRSA runs at each parent node to determine optimal data allocation to child nodes, and STRSD runs at each child node to determine optimal data de-allocation. The paper also proposes a intra-cluster replication algorithm called ORPNDA that uses heuristic expansion-shrinking methods to determine optimal partial data replication within each cluster. Experimental results show STRS and ORPNDA significantly outperform general frequency-based replication schemes.
An Efficient Compressed Data Structure Based Method for Frequent Item Set Miningijsrd.com
Frequent pattern mining is very important for business organizations. The major applications of frequent pattern mining include disease prediction and analysis, rain forecasting, profit maximization, etc. In this paper, we are presenting a new method for mining frequent patterns. Our method is based on a new compact data structure. This data structure will help in reducing the execution time.
Data mining is a very popular research topic over the years. Sequential pattern mining or sequential rule mining is very useful application of data mining for the prediction purpose. In this paper, we have presented a review over sequential rule cum sequential pattern mining. The advantages & drawbacks of each popular sequential mining method is discussed in brief.
An improved apriori algorithm for association rulesijnlc
There are several mining algorithms of association rules. One of the most popular algorithms is Apriori
that is used to extract frequent itemsets from large database and getting the association rule for
discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original
Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and
presents an improvement on Apriori by reducing that wasted time depending on scanning only some
transactions. The paper shows by experimental results with several groups of transactions, and with
several values of minimum support that applied on the original Apriori and our implemented improved
Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original
Apriori, and makes the Apriori algorithm more efficient and less time consuming
This document summarizes a research paper that proposes a new algorithm called ESW-FI to efficiently mine frequent itemsets from data streams using a sliding window model. The algorithm actively maintains potentially frequent itemsets in a compact data structure using only a single pass over the data. It guarantees output quality and bounds memory usage. The algorithm divides the sliding window into fixed-size segments and processes window slides by inserting new segments and removing old ones, avoiding reprocessing of all transactions on each slide.
This document summarizes an algorithm called ESW-FI that efficiently mines frequent itemsets from data streams using a sliding window model. The algorithm actively maintains potentially frequent itemsets in a compact data structure using only a single pass over the data. This is an improvement over existing algorithms that require multiple scans or maintaining all transaction data within the window. The ESW-FI algorithm guarantees output quality and bounds memory usage while processing streams of continuous, unpredictable data in a timely manner.
A Quantified Approach for large Dataset Compression in Association MiningIOSR Journals
Abstract: With the rapid development of computer and information technology in the last several decades, an
enormous amount of data in science and engineering will continuously be generated in massive scale; data
compression is needed to reduce the cost and storage space. Compression and discovering association rules by
identifying relationships among sets of items in a transaction database is an important problem in Data Mining.
Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore
it has attracted significant research attention. However, existing compression algorithms are not appropriate in
data mining for large data sets. In this research a new approach is describe in which the original dataset is
sorted in lexicographical order and desired number of groups are formed to generate the quantification tables.
These quantification tables are used to generate the compressed dataset, which is more efficient algorithm for
mining complete frequent itemsets from compressed dataset. The experimental results show that the proposed
algorithm performs better when comparing it with the mining merge algorithm with different supports and
execution time.
Keywords: Apriori Algorithm, mining merge Algorithm, quantification table
Similar to DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams (20)
A scalable collaborative filtering framework based on co clusteringAllenWu
This document proposes a scalable collaborative filtering framework based on co-clustering. It introduces collaborative filtering and discusses limitations of existing methods. The framework uses co-clustering to simultaneously obtain user and item neighborhoods and generate predictions based on average ratings. Experimental results show the approach provides high quality predictions with lower computational cost than other methods.
This document describes a collaborative filtering approach using co-clustering with augmented data matrices (CCAM). CCAM extends a co-clustering algorithm based on information theory to simultaneously cluster users, items, and additional data (e.g. user profiles, item features). The authors apply CCAM to collaborative filtering by using the co-clusters as prototypes for predicting user ratings. They tune CCAM's parameters on a dataset from an online advertising site and compare its mean absolute error to other collaborative filtering methods. CCAM outperforms k-means clustering, k-nearest neighbors, and information-theoretic co-clustering on this task.
Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of co-clustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Data Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked; both ads and users also have their feature data, i.e. the augmented data matrix. To evaluate the proposed method, we use two measures: classification accuracy and K-L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it consider the use of augmented data during clustering.
Chapter 4 of Data-Intensive Text Processing with Map Reduce introduce the efficiently map-reduce algorithm, pairs and stripes. It show how to use these two algorithm to contrust the co-occurrence matrix. It compare the time complexity between pairs and stripes algorithms. According to the experiments, the stripes algorithm have the better efficiency than pairs algorithm.
Collaborative filtering using orthogonal nonnegative matrixAllenWu
This document summarizes a research paper that proposes using orthogonal nonnegative matrix tri-factorization (ONMTF) to fuse model-based and memory-based collaborative filtering approaches. ONMTF is used to co-cluster users and items to obtain centroids that are then used to select similar users and items for predicting unknown ratings. Experimental results on movie rating datasets show the ONMTF approach improves prediction accuracy over other collaborative filtering methods.
1) The document presents a new co-clustering framework called Block Value Decomposition (BVD) for dyadic data. BVD factorizes a data matrix into three components: a row coefficient matrix, a block value matrix, and a column coefficient matrix.
2) An algorithm for non-negative BVD (NBVD) is derived based on minimizing the reconstruction error between the original and reconstructed matrices. The algorithm iteratively updates the three matrices using equations derived from Kuhn-Tucker conditions.
3) Empirical evaluations on text clustering datasets show NBVD achieves high clustering accuracy that is competitive with or better than other co-clustering algorithms.
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log
and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views the contingency table as an empirical joint probability distribution of two discrete random variables and poses
the co-clustering problem as an optimization problem in information theory — the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters. We present an innovative co-clustering algorithm
that monotonically increases the preserved mutual information by intertwining both the row and column clusterings at all stages. Using the practical example of simultaneous
word-document clustering, we demonstrate that our algorithm works well in practice, especially in the presence of sparsity and high-dimensionality.
Semantics In Digital Photos A Contenxtual AnalysisAllenWu
Interpreting the semantics of an image is a hard problem. However, for storing and indexing large multimedia collections,
it is essential to build systems that can automatically extract semantics from images. In this research we show how we can fuse content and context to extract semantics from digital photographs. Our experiments show that if we can properly model context associated with media, we can interpret semantics using only a part of high dimensional content data.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
1. DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams Presenter / Meng-Lun Wu Source / ICDM’06, IEEE Author / Carson Kai-Sang Leung, Quamrul I. Khan