In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
In this you know about
Types of Data Structures / Data structures types in C++
1.Primitive and non-primitive data structure
2.Linear and non-linear data structure
3.Static and dynamic data structure
4.Persistent and ephemeral data structure
5.Sequential and direct access data structure
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
Stefan & Irene photo with team at Cinnamon hotel Saigon. They send the Thank you letter to Cinnamon Hotel Saigon for their beautiful, enjoyable stay in December 2012. They appreciate the nice atmosphere, delicious breakfast, the organic attention to client . Also they enjoy the lovely rooms and especially the exceptional friendliness of Cinnamon Hotel team. Visitor at Cinnamon Hotel feel entirely welcome. They adore als the piece of craft art the team made to them. Stefan and Iren wish Cinnamon Hotel a Happy New Year 2013 and all the best for the team and their family of Cinnamon Hotel.
In today’s world there is a wide availability of huge amount of data and thus there is a need for turning this
data into useful information which is referred to as knowledge. This demand for knowledge discovery
process has led to the development of many algorithms used to determine the association rules. One of the
major problems faced by these algorithms is generation of candidate sets. The FP-Tree algorithm is one of
the most preferred algorithms for association rule mining because it gives association rules without
generating candidate sets. But in the process of doing so, it generates many CP-trees which decreases its
efficiency. In this research paper, an improvised FP-tree algorithm with a modified header table, along
with a spare table and the MFI algorithm for association rule mining is proposed. This algorithm generates
frequent item sets without using candidate sets and CP-trees.
Statistics is both the science of uncertainty and the technology.docxrafaelaj1
Statistics is both the science of uncertainty and the technology of extracting information from data.
A statistic is a summary measure of data.
Descriptive statistics are methods that describe and summarize data.
Microsoft Excel supports statistical analysis in two ways:
1. Statistical functions
2. Analysis Toolpak add-in
Statistical Methods for Summarizing Data
A frequency distribution is a table that shows the number of observations in each of several nonoverlapping groups.
Categorical variables naturally define the groups in a frequency distribution.
To construct a frequency distribution, we need only count the number of observations that appear in each category.
This can be done using the Excel COUNTIF function.
Frequency Distributions for Categorical Data
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
List the item names in a column on the spreadsheet.
Use the function =COUNTIF($D$4:$D$97,cell_reference), where cell_reference is the cell containing the item name
Example 3.16: Constructing a Frequency Distribution for Items in the Purchase Orders Database
Construct a column chart to visualize the frequencies.
Relative frequency is the fraction, or proportion, of the total.
If a data set has n observations, the relative frequency of category i is:
We often multiply the relative frequencies by 100 to express them as percentages.
A relative frequency distribution is a tabular summary of the relative frequencies of all categories.
Relative Frequency Distributions
Example 3.17: Constructing a Relative Frequency Distribution for Items in the Purchase Orders Database
First, sum the frequencies to find the total number (note that the sum of the frequencies must be the same as the total number of observations, n).
Then divide the frequency of each category by this value.
For numerical data that consist of a small number of discrete values, we may construct a frequency distribution similar to the way we did for categorical data; that is, we simply use COUNTIF to count the frequencies of each discrete value.
Frequency Distributions for Numerical Data
In the Purchase Orders data, the A/P terms are all whole numbers 15, 25, 30, and 45.
Example 3.18: Frequency and Relative Frequency Distribution for A/P Terms
A graphical depiction of a frequency distribution for numerical data in the form of a column chart is called a histogram.
Frequency distributions and histograms can be created using the Analysis Toolpak in Excel.
Click the Data Analysis tools button in the Analysis group under the Data tab in the Excel menu bar and select Histogram from the list.
Excel Histogram Tool
Specify the Input Range corresponding to the data. If you include the column header, then also check the Labels box so Excel knows that the range contains a label. The Bin Range defines the groups (Excel calls these “bins”) used for the frequency distribution.
Histogra.
A Performance Based Transposition algorithm for Frequent Itemsets GenerationWaqas Tariq
Association Rule Mining (ARM) technique is used to discover the interesting association or correlation among a large set of data items. it plays an important role in generating frequent itemsets from large databases. Many industries are interested in developing the association rules from their databases due to continuous retrieval and storage of huge amount of data. The discovery of interesting association relationship among business transaction records in many business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. It is also used to extract hidden knowledge from large datasets. The ARM algorithms such as Apriori, FP-Growth requires repeated scans over the entire database. All the input/output overheads that are being generated during repeated scanning the entire database decrease the performance of CPU, memory and I/O overheads. In this paper, we have proposed a Performance Based Transposition Algorithm (PBTA) for frequent itemsets generation. We will compare proposed algorithm with Apriori algorithm for frequent itemsets generation. The CPU and I/O overhead can be reduced in our proposed algorithm and it is much faster than other ARM algorithms.
Data Mining Exploring DataLecture Notes for Chapter 3OllieShoresna
Data Mining: Exploring Data
Lecture Notes for Chapter 3
Introduction to Data Mining
by
Tan, Steinbach, Kumar
What is data exploration?Key motivations of data exploration includeHelping to select the right tool for preprocessing or analysisMaking use of humans’ abilities to recognize patterns People can recognize patterns not captured by data analysis tools
Related to the area of Exploratory Data Analysis (EDA)Created by statistician John TukeySeminal book is Exploratory Data Analysis by TukeyA nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook
http://www.itl.nist.gov/div898/handbook/index.htm
A preliminary exploration of the data to better understand its characteristics.
Techniques Used In Data Exploration In EDA, as originally defined by TukeyThe focus was on visualizationClustering and anomaly detection were viewed as exploratory techniquesIn data mining, clustering and anomaly detection are major areas of interest, and not thought of as just exploratory
In our discussion of data exploration, we focus onSummary statisticsVisualizationOnline Analytical Processing (OLAP)
Iris Sample Data Set Many of the exploratory data techniques are illustrated with the Iris Plant data set.Can be obtained from the UCI Machine Learning Repository
http://www.ics.uci.edu/~mlearn/MLRepository.htmlFrom the statistician Douglas FisherThree flower types (classes): Setosa Virginica VersicolourFour (non-class) attributes Sepal width and length Petal width and length
Virginica. Robert H. Mohlenbrock. USDA NRCS. 1995. Northeast wetland flora: Field office guide to plant species. Northeast National Technical Center, Chester, PA. Courtesy of USDA NRCS Wetland Science Institute.
Summary StatisticsSummary statistics are numbers that summarize properties of the data
Summarized properties include frequency, location and spread Examples: location - mean
spread - standard deviation
Most summary statistics can be calculated in a single pass through the data
Frequency and ModeThe frequency of an attribute value is the percentage of time the value occurs in the
data set For example, given the attribute ‘gender’ and a representative population of people, the gender ‘female’ occurs about 50% of the time.The mode of a an attribute is the most frequent attribute value The notions of frequency and mode are typically used with categorical data
PercentilesFor continuous data, the notion of a percentile is more useful.
Given an ordinal or continuous attribute x and a number p between 0 and 100, the pth percentile is a value of x such that p% of the observed values of x are less than .
For instance, the 50th percentile is the value such that 50% of all values of x are less than .
Measures of Location: Mean and MedianThe mean is the most common measure of the location of a set of points. However, the mean is very sensitive to outliers. ...
The D-basis Algorithm for Association Rules of High ConfidenceITIIIndustries
We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data
Running Head PROJECT DELIVERABLE 31PROJECT DELIVERABLE 310.docxtodd581
Running Head: PROJECT DELIVERABLE 31
PROJECT DELIVERABLE 310
Project Deliverable 3: Database and Programming Design
Leo Austin
Professor Joe Scott
CIS498 – Information Technology Capstone
08/22/2018
Introduction
Bicycle Trader being a constantly growing internet-based company requires the collection of an abundance of data to analyze for continued operations. Whether customers signup for services or browse through the website, data is gathered to allow the website to adapt to demands and cater to the customers’ needs and determine what will make using the site more user-friendly. Most importantly is the need to gather data in order to facilitate the entry and archiving of customer input data and use by other entities or departments within the business. Various database models can be taken into consideration for the needs of this business, and the relational database model is the most applicable due to the data sorting requirements for the website.
Not only is the rational database model the ideal database solution, but because they primarily consist of tables used to manage and store data, they are relatively easy to create and maintain. Many organizations choose this approach as it facilitates access to understandable data assets. Separating data by implementing tables also allows for the ability to adequately secure data by distinguishing each with their own classifications. Sorting data into tables also means that data can be added or withdrawn without having to overhaul the entire database.
Implementing data warehousing alongside relational databases provides further practicality and presents many advantages. By doing so, we can take advantage of its ability to “store large quantities of historical data and enable fast, complex queries across all the data, typically using Online Analytical Processing (OLAP)” (Panoply, n.d.). Data warehouses are essentially a collection of data from various sources that can be used by organizations for reporting and analysis. Because of the nature of Bicycle Trader and the abundance of like items that will be sold be by users on the website, a data warehouse will be the most practical solution for archiving data, because unlike most databases which normalize data in order to eliminate redundant data, a data warehouse uses a denormalized data structure. This means that fewer data tables with more grouping are used and redundancies aren’t excluded.
This combination of relational data systems, the data warehouse and relational database, can be hosted internally by the organization on its’ mainframe, and stored in their cloud. Using a cloud yields more advantages as it is the easiest and most cost-effective approach. By using this method, data can easily be accessed from several locations. Additionally, this allows for fewer physical resources as it eliminates some of the costs associated with expensive systems and equipment, expert staff, and energy consumption by alternatively utilizing the .
Market Basket Analysis of Database Table References Using RJeffrey Tyzzer
Market basket analysis and statistical and informetric analyses are applied to a population of database queries (SELECT statements) to better understand table usage and co-occurrence patterns and inform placement on physical media.
The Role of Histograms in Exploring Data InsightsCIToolkit
A graph which shows the frequency of continuous data values. Histograms are mainly used to explore data as well as to present the data in an easy and understandable manner. They are often used as the first step to determine the underlying probability distribution of a data set or a sample.
Introduction To Multilevel Association Rule And Its MethodsIJSRD
Association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. In this paper we introduce the concept of Data mining, Association rule and Multilevel association rule with different algorithm, its advantage and concept of Fuzzy logic and Genetic Algorithm. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
National Security Agency - NSA mobile device best practices
XL-MINER: Associations
1. Introduction to XLMiner™: ASSOCIATION AND CHARTS XLMiner and Microsoft Office are registered trademarks of the respective owners.
2. ASSOCIATION Association rules are used to find out the interesting and useful relationships between data that occur frequently enough to be called a pattern (or a trend) and hence, can be formulated into a rule. Each of these rules has an if-then structure with an antecedent and a consequent and has three properties associated with it – support, confidence and lift. Support is the number of records that contain both the antecedent and consequent i.e. the number of records for which the rule holds true. Confidence is the ratio of the support to the number of the records where the antecedent occurs (i.e. a ratio of the number of records where the rule holds true to the total number of records where antecedent occurs). The third parameter is the lift. Lift = confidence/ (ratio of the number of records containing the consequent to the total number of records) http://dataminingtools.net
3. ASSOCIATION If the data in our table is in form of 0 and 1 the wizard by default selects the “data in binary matrix format". We may choose to override this. http://dataminingtools.net
4. ASSOCIATION The conf,% of 52.89% represents that of all the persons who bought a “refbook” 52.89% bought Childbk and cookbks together. Support (a)shows number of transactions containing refbks and childbks, while Support(c ) shows number of transactions containing refbks. http://dataminingtools.net
5. CHARTS Charts allows us to view the data in a visual fashion so as to interpret it easily. Many sheets are created during drawing models but are kept hidden. To delete them select the “Delete hidden sheets “. XLMiner provides us with three different methods to view data: Box plot Histogram Matrix plot http://dataminingtools.net
10. Maximum data valueAlso, the box plot is not affected by outliers - i.e. inconsistent or aberrant data. It is also used to compare values. DATA SET http://dataminingtools.net
11. CHARTS – BOX PLOT Since the X-Var in the data set holds 2 values(3 and 4) 4 boxes one for each value of Y1 and Y2 are drawn. The notch-height represents the confidence interval around the mean. When we de-check "Notched" we do not expect the confidence interval to be displayed http://dataminingtools.net
12. CHARTS – HISTOGRAM Histogram:A histogram is a bar graph. It has frequency of occurrence on the Y axis and the variable to be examined on the X axis. Histograms are popular among statisticians. Though they do not show the exact values of the data points they give a very good idea about the spread of the data and shape. http://dataminingtools.net
13. CHARTS – HISTOGRAM This histogram shows the minimum and maximum values . The tools decides the number of intervals .Here there are 11 intervals. Each bar represents the frequency of that value in the data set. http://dataminingtools.net
14. CHARTS – MATRIX PLOT A Matrix plot is a kind of Scatter Plot which enables the user to see the pair wise relationships between variables. XLMiner� allows eight variables to be plotted against each other at a time DATA SET http://dataminingtools.net
15. CHARTS – BOX PLOT The dots represent the values of variables. To find the actual value multiple the value on graph (refer the scale ) to the multiplier (for e.g. 102 in case of AGE) . http://dataminingtools.net
16. Thank you For more visit: http://dataminingtools.net http://dataminingtools.net
17. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net