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Big Data Presentation

Transcript: Case study - the use of facial recognition technology at Amsterdam Schiphol Airport, a pilot project Paula Vermelho Cordiolli, Ingrid Quinn, Melike Babur Baran Introduction & Overview Scope & limitations of fundamental rights as applied to use of FRT (for national security, border control) Use of FRT: Compliance-Non-compliance with EU HR Law Private and public sector obligations Challenges, gaps, opportunities Analysis and Conclusion Introduction Main stakeholders: Amsterdam Schiphol Airport Royal Netherlands Marechaussee (KMar) Cathay Pacific Vision Box Introduction Introduction Main points: The program's goal is the development of self-boarding technology, by which the passengers will be able to pass through the Schiphol checkpoints by facial recognition Procedure Regulation EU laws & regulations Treaty on European Union (TEU) Main aspects: Human Rights Data Protection Border Control Other relevant principles: Respect for private life and protection of personal data Non-discrimination Rights of the child and of elderly people Freedom of expression and freedom of assembly and of association Right to good administration Right to an effective remedy European Charter of Human Rights (ECHR) Article 8: Right to respect for private life and family life Article 14: Prohibition of discrimination Charter of Fundamental Rights of the European Union (CFR) Article 7: Right to respect for private and family life Applicable EU laws, regulations and directives regarding Human Rights Human Rights Treaty on European Union (TEU) Article 4: Processing data carried out by national intelligence and military agencies falls outside EU law Treaty on Functioning of the European Union (TFEU) Article 16: Legal basis of protection of personal data Applicable EU laws, regulations and directives regarding Data Protection Data Protection Regulation 2017/2226 Established an Entry/Exit System (EES) to register entry and exit data and refusal of entry data of third-country national crossing external boarders of Member States Determined the conditions for access to the EES for law enforcement purposes Applicable EU laws, regulations and directives regarding Border Control Border Control Scope & limitations of fundamental rights as applied to use of FRT (for national security, border control) Use of FRT: Compliance-Non-compliance with EU HR Law Private and public sector obligations Challenges, gaps, opportunities Analysis and Conclusion Analysis and conclusion Main stakeholders: Amsterdam Schiphol Airport Royal Netherlands Marechaussee (KMar) Cathay Pacific Vision Box Introduction Introduction Main points: The program's goal is the development of self-boarding technology, by which the passengers will be able to pass through the Schiphol checkpoints by facial recognition Procedure Regulation Schiphol Airport case study Treaty on European Union (TEU) Main aspects: Human Rights Data Protection Border Control Other relevant principles: Respect for private life and protection of personal data Non-discrimination Rights of the child and of elderly people Freedom of expression and freedom of assembly and of association Right to good administration Right to an effective remedy ECHR - European Charter of Human rights Article 8: Right to respect for private life and family life Article 14: Prohibition of discrimination CFR - Charter of Fundamental Rights of the European Union Article 7: Right to respect for private and family life Applicable EU laws, regulations and directives regarding Human Rights Human Rights GDPR - General Data Protection Regulation Article 4: "Biometric Data'' Article 5: Principles relating to the processing of personal data Article 6: Lawfulness of processing Article 9: Processing of Biometric Data Article 22: Automated individual decision-making LED - Law Enforcement Directives 2016/680 Article 3 and 10 (same as in GDPR) Regulation 2018/1725 TEU - Treaty on European Union ARTICLE 4: Processing data carried out by national intelligence and military agencies falls outside EU law TFEU - Treaty on Functioning of European Union Article 16: Legal basis of protection of personal data Applicable EU laws, regulations and directives regarding Data Protection Data Protection Regulation 2017/2226 Established an Entry/Exit System (EES) to register entry and exit data and refusal of entry data of third-country national crossing external boarders of Member States Determined the conditions for access to the EES for law enforcement purposes Applicable EU laws, regulations and directives regarding Border Control Border Control

Big Data Presentation

Transcript: Big Data Relating to Course Work Big data enters into the fourth industrial revolution that includes agile governance, inequality, security and conflict, innovation and productivity, ethics and identity, business disruption, disruption to job and skills, and fusing technologies. A breakdown of data: Data lives in warehouses and storage farms and it collects, propagates and breeds new data as well as aligns patterns to create intelligent predictions. There are a lot of true data assumptions about the way your information is used. For example: 49% of American adults who use the internet incorrectly believe that by law a supermarket must obtain a person's permission before selling info about that person's food purchases. Applications of Big Data Cont. Applications of Big Data and Impacts of Giving Data When data gives us something, we probably don't think about what it takes from us in return. Data gives us: - social networks, mapping services, ability to make calls, moble apps to control homes, shopping, voice control, genetic cataloging, and much more. However, data also takes from us: - facial recognition, blood testing, medical info, movement tracking, and associative behavior. Applications of using Big Data are all around. A very common application of Big Data is in healthcare. Big Data analytics have improved healthcare industries. They have succeeded in doing so by providing personalized medicine and perscriptive analytics, and researching what kind of treatments are more effective for certain conditions and with that they have identified patterns related to drug side effects and provide a solution at a reasonable cost. eHealth and mHealth also increase the volume of data which includes electronic health records, fitness trackers, research data, etc. A real world example of using big data in clinical analytics is when healthcare industries wanted to replace the data warehousing solution with one that could manage high volumes of data. In order to do this, they hired Citius Tech in which they designed a solution. It injects diagnostic results and billing messages into the data injestion stream undergoing Apache Spark Streaming which produces real time data streams. These records are landed onto the cloud where it undergoes deduplicating and standarization of the data. It is then processed using MapReduce for processing of data. Finally, it populates and stores the messages into a IBM unified data model for healthcare organizations. Introduction to Big Data and Its Impact Big Data Presentation Big Data is referred to as large volumes of data both structured and unstructured, that inundate a business on a day-to-day basis. It's not the amount of data that's important, but what organizations do with it. Big data can be analyzed for insights that lead to better decisions and strategic business moves. Big data can have a significant impact across various areas, including: Business: Big data analytics can help businesses understand customer behavior, improve operational efficiency, and identify new marke opportunities. Healthcare: it can enhance patient care by analyzing medical records, identifying disease patterns, and predicting outbreaks. Finance: Big data can improve risk management, fraud detection, and customer service in the financial sector. Transportation: It can optimize routes, reduce congestion, and improve safety in transportation systems. Education: Big data analytics can personalize learning experiences,track student progress, and identify areas for improvement. Government: it can improve public services, optimize resource allocation, and enhance decision making in policy development. Science and Research: Big data enables researchers to analyze large datasets, discover new patterns, and make breakthroughs in fields like genomics, climate science, and astronomy. Overall, big data has the potential to revolutionize various aspects of society by providing valuable insights and driving innovation. Conclusion Put something important here Summarizing Key Takeaways and Lessons Learned Provide more context here Unlocking Value and Addressing Ethical Challenges Conclusion Challenges & Opportunities 02 Cases & Applications Ethical Considerations 01 03 Addressing ethical considerations and implications of big data usage. Sum up the key take-aways and wrap up your presentation Examining relevant cases and applications of big data in various sectors. Analyzing ethical challenges and unlocking the value and opportunities of big data.

Big Data Presentation

Transcript: Digital Marketing Strategy for Digital Video Commercial The use of local country census and demographic data designed to identify and reach new retail prospects. We can target where the best prospects live across Africa Overlay where they live, with the best accessible retail locations in the prospects geographic footprint Seen as an archaic brand Low top of mind awareness for Philips. Mistaken for other Phillips Brands (Phillips Consulting, Phillips Engineering, Phillips Petroleum) especially among the younger demographic. Outcome: Awareness & Participation Target Audience Size 1,000,000+ Source: Social Baker Government – Ministries, Government Agencies Every good conversation starts with good listening. Google display ads Technology Trends Port Harcourt Twitter impressions Our role as digital marketers, is to navigate Philips’s aggressive reentry into the West Africa marketplace by executing our robust digital messaging strategy using C.I.D.M. Interpreting this brand position from an emotional perspective into purchase action in the West African marketplace Enjoy quality and good design at a price Google Display Network, Facebook Ad, and LinkedIn Ad The mobile phone is fast becoming as much an African symbol. 71% of adults in Nigeria, 62% in Botswana and more than half the populations of Ghana and Kenya own a mobile phone. Africa is the world's fastest-growing mobile phone market. It's 600m users make it second only to Asia. Government/NGO officials City Focus - No Activity Philips has a great heritage dating back to 1891 and plays on the global stage Has a strong track record of creating quality innovative products The brand is synonymous with quality and reliability. Cosmopolitan, Urban elegant and classical Search Engine Optimization (Organic Search) Paid Search (PPC Keyword Buying) Nearly 20 percent of Lagosians are now members of Nigeria’s growing middle class. Creative execution Sales Targeting What do you call a bulb? (Campaign) 2. Commercials for Lighting 2. SEO = Monthly impressions Driving purchase consideration and positive attributes towards the Philips brand and products by consumers. Sophisticated health conscious lifestyles Abuja Current Online Activity we plan on doing this by: Facebook RATIONALE Frying, roasting, grilling and baking with the Philips Airfryer is possible with much less fat than the usual frying/ deep frying method. The varied mix of executions highlight these benefits. Most visited sites via Mobile internet Inform Philips is part of our community, communities are made up of people. People use Philips products. As such ... Target Audience Recreate awareness of the Philips brand Develop and implement digital assets/capabilities that will be the bedrock of a comprehensive digital marketing campaign for the company brand and business groups Define the Philips brand tonality and manner across various digital assets Build and nurture the Philips online consumer community Aforementioned objectives must results in actionable sales Sponsored Post The Lighting section provides the following information: 1. Guidance for target consumers to make the right CFL bulb choice for their purpose (as choosing one can be a confusing task from the multiple of options available. (one of the steps to choosing a bulb was displayed; the other steps being choose your bulb base, choose your light output and choose your light control) The Competition Target Audience Size 1,000,000+ We want to make users like and trust us. That means listening to what users have to say and having thoughtful conversations about things they care about. Connections are people and channel affiliations we collaborate with in order to make sure we’re reaching the right users in the right places. Retail sales strategy planning Marketing campaign planning Public relations strategy planning Media buying and placement Real estate planning and much more... Understanding of the West African (Nigeria/Ghana) Markets and Digital Landscape Mobile Data Penetration Rate Facebook Microsite Innovation Work Group Ideas 5. E-Commerce Target Audience Size 100,000+ Source: Social Baker 6 Mobile internet users account for 60.69% of the Nigerian Internet population while 30.31% surf the internet using Desktops. Mobile penetration has been largely responsible for the accelerated growth in internet subscribers Search Engine Optimization (Organic Search) Paid Search (PPC Keyword Buying) The joy of becoming a parent Knowing that all is well with the baby The confidence that comes with a better diagnosis 3 Billion CFL Campaign Lighting $ 3. A demonstration of what lighting can do for various businesses for their walls, ceilings and outdoor (demos have been included for some businesses; the demos are positional and not the actual lighting options for these various spaces) Takoradi An average Nigerian spends 3hours surfing the internet daily - Low Activity Facebook Ad Upper middle and upper social economic class Consumer Insight + SMP Benefit

Big Data Presentation

Transcript: A Big Data Presentation C B - Amal Bazilah (19b3110) - Amal Ummi Naqibah (19b3083) - Batrisyia Safwah (19b3108) Topic: Financial Aid (Monthly Welfare Assistance) -Siti Zahidah (19b3113) Introduction 1) Many people applied for the Monthly Welfare Assistance : - Receive more than they genuinely require (dependency) - Receive less/ insufficient financial support for their necessities Problems : Background 2) Difficulty in applying for some people (tedious & time consuming) 3) Dishonesty in presenting relevant documents & living condition Provided by? - Jabatan Pembangunan Masyarakat ( Department of Community Development) JAPEM "Help alleviate the burden of an individual or family who are living in difficulty and helplessness" Improvement needed in assessing: 1) Expenses 2) Number of children 3) Minimum living conditions (Achieved?) 4) Income 5) Application system How application currently works: Submission of letter to director of JAPEM or village heads; directly visit the headquarters Documents should be included with the letter (such as...) JAPEM Jabatan Pembangunan Masyarakat; department of Community Development under ministry of culture youth & sports The need for big data To improve the efficiency of the system To improve the efficiency of the system Current system: 1) Monthly investigation is done on the recipients to ensure the extend of which they are eligible Based on: -Having no income -Difficulty in living and the number of children who are still in school Reduction / Termination of payment to recipients: -Married or working. -Wife is working and is considered to be able to help in every day living. -Received assistance from government agencies or non-governmental agencies. -Means of livelihood that can support their families. -Numerical data - taken via different database from the government & private sector e.g records and data phone number. -The data - made by both human and machine, generated and depend on a data model. -Sources data is collected from - SQL databases, online forms, sensors (GPS), Network and Web server logs and medical devices. Structural data Type of big data needed -It is easily recorded, accessed and stored -More effective processing and analysis -Data structure is well defined and data can be easily updated or deleted -Data mining is made easier -Ensuring security to data is easier Why it is used? Advantages -In the past, due to limited storage and the cost, it is collected using relational databases and spreadsheets to effectively manage data. -It is now often managed using SQL (Structured Query Language) as it is quick and efficient. How it is collected? Methods Where & how big data will be collected Application: 1) Personal Information are inserted by trusted employees in the respective departments/ministries 2) Income of the parents ( from employers) 3) Minimum expenses that each family/ family members require. Source of info Information are derived from various sources - big data How will the data be analysed? - Data such as the minimum monthly expense of each family member is collected, estimated and compiled together. - The system will automatically calculate the total needs and requirements of the family - Any family with living conditions that requires the greatest care will be prioritized Family member - The minimum quantity of food needed for each member (minimum calories needed based on their BMI and BMR) - School expenses for each child (books, stationeries, uniform, school fees) - medical needs (unprovided medicines) - Special needs (wheelchairs, therapy products, assistive technology) Whole family Car fuel House bills: - Water bills -Electricity bills -Groceries Expected results A more confidential, secure & reliable system of financial tracking and personal data, (for the process of financial aid) Comparison Expected results /improvement Example Of

Big Data Presentation

Transcript: BIG DATA Humanistic Perspective on Data Case Study: Who Uses Big Data? Marketing Target interests Intelligence Gathering Profiling Product Improvement Feedback The 3 V's of Big Data Alexis Gregerson, Roshni Changela, Michael McKeirnan, Sean Dolan, Sanjay Sagar, Sun Kim http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&docid=34uDaDViB81vTM&tbnid=1hpymMnLVe05pM:&ved=0CAUQjRw&url=http%3A%2F%2Fwww.saldef.org%2Fnews%2F7872%2Fattachment%2Fcomcast-logo%2F&ei=TsUKU7m9Es66oQTJgoGAAg&bvm=bv.61725948,d.cGU&psig=AFQjCNFLGv4hckLYRETjtxxyKBXawGSbag&ust=1393301189739839 Find the interactive heat map here: http://mckeimic.com/info What does Big Data Mean for you? Infographics Display vast amounts of data Benefits Engage consumers Allow new discoveries Risks Potentially misleading Can lead to false discoveries Big data is being monitored for potential threats Uses Accumulo to "tag data" Noise is coming faster than the Signal Success rate is low Is our invasion of privacy worth it? How Big is Big Data? Lecture Group 17: What is Big Data used for? Washington Voter Registration Healthcare: 20% decrease in patient mortality by analyzing streaming patient data Telco: 92% decrease in processing time by analyzing networking and call data Utilities: 99% improved accuracy in placing power generation resources by analyzing 2.8 petabytes of untapped data -(IBM) http://www.economist.com/node/15557443 http://whatis.techtarget.com/definition/3Vs http://www-01.ibm.com/software/data/bigdata/industry.html http://www.oxforddictionaries.com/us/definition/american_english/infographic http://www.fastcodesign.com/1669222/a-case-study-in-how-infographics-can-bend-the-truth http://www.healthcarecommunication.com/Main/Articles/7_common_problems_with_infographics_9536.aspx http://www.healthcarecommunication.com/Main/Articles/7_common_problems_with_infographics_9536.aspx http://www.nytimes.com/2011/04/03/business/03stream.html?_r=3& http://www.google.com/imgres?client=safari&rls=en&biw=1129&bih=538&tbm=isch&tbnid=Oi0dU87Q224zwM%3A&imgrefurl=http%3A%2F%2Fsac.unm.edu%2F&docid=BbGU6Scehn4xOM&imgurl=http%3A%2F%2Fsac.unm.edu%2Fimages%2Flongtermfiles%2Ffacebook_logo.jpg&w=2197&h=1463&ei=E8QKU-SaKI3roATw54KYAg&zoom=1&ved=0CGcQhBwwAA&iact=rc&dur=567&page=1&start=0&ndsp=8 http://ron-tornambe.bunkerhill.com/images/clients/ibm-logo.png Closing So is it working? Silicon Valley (Twitter feed??) Summary NSA "Data will be the next natural resource" -Ginni Rometty, IBM CEO BIG DATA: WORKS CITED What comes to mind when you hear big data? A collection of most often large and complex data sets Difficult to process using traditional data processing systems and applications Interviews here Example: Correct Numbers Represented NOW Bag gives user impression that debt has quadrupled in size

Big Data Presentation

Transcript: Suggestions and Ideas to improve existing model Exposure Apart from Curicullum Exposure Apart from Curicullum More Indusrial Visits More Indusrial Visits More Industrial visits should be organised to the core compani... More Industrial visits should be organised to the core companies of particular specialisations such as Mu Sigma (Big Data) so that the students could get more exposure of the Corporate sector and could better understand the practical and live working of their specialisation companies. Seminars Except the Technical Training Seminars Except the Technical Training Expert Practical Sessions on various technologies . More hands on experience More knowledge on Who an Industry Works .i.e.An inside view of a corporate TIMELINE TIMELINE 2017 MAP MAP CHART Label 1 Label 2 Label 3 Label 4 CHART More Help in Placements More Help in Placements Conduct Mock INterviews and GD's Conduct Mock INterviews and GD's Internship in IBM Internship in IBM Internship in IBM should be provided to IBM Specialised Students in their specific Specialisations Our SGPA less than CSE Our SGPA less than CSE Due to less number of students ,Students of IBM-C... Due to less number of students ,Students of IBM-CSE have lesser CGPA than CSE Students . So,Eligibilty Criteria for CSE and CSE -IBM should differ and should not be o the basis of CGPA. Change in IBM Resources Change in IBM Resources Study Material to be changed Study Material to be changed The IBM Study material is not up to the mark. The Other reference books are more useful than IBM Books. The IBM Study material just contains the Presentations with some Explanation and are very less useful. Specialised LAbs Required Specialised LAbs Required More Labs required with systems having more than 8 GB RAM . Labs with Specialised Softwares for Each Specialisation . As of now,We have only one lab with Systems having 8GB RAM and Many IBM Tools require 8 GB RAM. Engineering Practice Engineering Practice *Since,the test is just of 1 hour and EP lab has two lectures .So,we have to sit idle for one lecture. *EP should be removed from the curicullum as it isn't benifitting us . *If,removing isn't possible,then reduce it to one lecture so that we could do something Productive. Other KEY POINTS Other KEY POINTS *Transport Facilities for day scholars after TPP extra classes. *Library of Block-9 should be available till 8 pm and should include books of our specialisations. *Inter departmental Sports meet should be promoted and Some Fun Activities should also be conducted. Some Free Lectures Some Free Lectures *Some Free lectures should be introduced in our time table (ATLEAST ONE DAY) ,because our Time Table is jam-packed and we have no free time to visit other teachers or departments .

Big Data Presentation

Transcript: Big Data, Data Science and Machine learning Independent Project Topic: Customer Segmentation Classification By E. Gbogbo and P. Alikizang Introduction To obtain your certificate at TamTam Digital School, you need to present an independent project. We have chosen the topic entitled: Customer Segmentation Classification. TamTam Digital School Contents Customer Segmentation Classification Our goal is to help an automobile company to classify its new customers in 4 categories which are identical to its former customers in order to increase the performance of that company. 1 Implementation Tools We have based our work on what we learnt during our training courses: Python Scikit-learn Implementation Tools MatplotLib Pandas Seaborn 2 TamTam Digital School Data Preprocessing Data Preprocessing Data preprocessing refers to the manipulation or dropping of data before it is used in order to ensure and enhance performance, and is an important step in the data mining process. TamTam Digital School 3 Exploratory Data Steps Importing Libraries and loading data Finding and Removing Duplicate Rows from the train and test dataset if present. Steps for Exploratory Data Dropping or Filling null or Missing Values Visualization and interpretation Tam-Tam Digital School. 4 Features Engineering Encoding data Features Engineering Splitting Data for training and validation Tam-Tam Digital School. 5 Building models At this step, we wrote the logic of our model which enabled us to predict the categories of new users registered in the system. Building Model Here are the three libraries: LGBMClassifier 6 TamTam Digital School XGBClassifer XGBoost provides a wrapper class to enable models to be treated like classifiers or regressors in the scikit-learn framework.The XGBoost model for classification is called XGBClassifier. XGBClassifer Tam-Tam Digital School. 8 LGBMClassifier LGBMClassifier is a module from LightGBM which is a fast, distributed, high performance gradient boosting framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. Tam-Tam Digital School. 7 RandomForestClassifier A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. RandomForestClassifier Tam-Tam Digital School. 9 Demo Demo 10 TamTam Digital School https://user-segmentation.herokuapp.com/ Conclusion Conclusion With our model, it is possible to classify users (customers) upfront and offer them personalized products as this could increase the company's sales and revenue by satisfying them while ensuring excellent quality of products and services. We have put into practice our skills and knowledge received during our training to achieve the result obtained. It was really beneficial for us to work on a project that gave us an overview of what a professional environment looks like. 11 TamTam Digital School Prospects Prospects 12 TamTam Digital School Our prospects are to continue collecting data, ensuring it and making sure we enhance the performance of the model under consideration. As IT is a fast innovating field of knowledge, it is within our interest to work hard towards getting other tools or technics likely to help us get higher accuracy for this specific model. Thank you for your keen attention. Your critical remarks and amendments will be duly taken into consideration and acted on so as to improve the scientific quality of this piece of work. 13  <svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:a="http://ns.adobe.com/AdobeSVGViewerExtensions/3.0/" x="0px" y="0px" width="774px" height="115.4px" viewBox="0 0 774 115.4" style="enable-background:new 0 0 774 115.4;" xml:space="preserve"> <style type="text/css"> .st0{fill:#FFFFFF;} .st1{fill:url(#SVGID_1_);stroke:#047391;stroke-width:0.3288;stroke-linecap:round;} .st2{fill:#F4C327;stroke:#006B33;stroke-width:1.3154;stroke-dasharray:1.9731,0.8221;} .st3{fill:none;stroke:#0060B6;stroke-width:0.6577;stroke-linecap:round;} .st4{fill:#FF7F00;stroke:#BF0000;stroke-width:0.1644;} .st5{fill:#60F475;stroke:#BF0000;stroke-width:0.1415;} .st6{fill:none;stroke:#006B33;stroke-width:0.283;stroke-linecap:round;} .st7{fill:#FF4000;} .st8{opacity:0.23;fill:url(#SVGID_2_);enable-background:new ;} .st9{fill:url(#SVGID_3_);stroke:#047391;stroke-width:0.3191;stroke-linecap:round;} .st10{opacity:0.43;fill:url(#SVGID_4_);stroke:#006B33;stroke-width:1.8524;stroke-dasharray:1.1578,3.4733;enable-background:new ;} .st11{fill:none;stroke:#003CFF;stroke-width:0.4787;stroke-linecap:round;} .st12{fill:#FF7F00;stroke:#BF0000;stroke-width:0.1596;} .st13{opacity:0.62;} .st14{fill:#60F475;stroke:#BF0000;stroke-width:0.1373;} .st15{fill:none;stroke:#006B33;stroke-width:0.2747;stroke-linecap:round;}

Big Data Presentation

Transcript: BIG DATA Thank you for your attention! QUESTIONS? 20/10/2021 Attila Varga AGENDA AGENDA What is Big Data? Why to use Big Data? Applications Of Big Data - Examples Try it yourself! - Google Earth Engine example References Types of data What is Big Data? WHAT IS BIG DATA? What is data? The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media. BASIC INFORMATION What is Big data? Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size. THE 4V'S The 4V's of Big Data V V V V BIG DATA TYPES Types of Big Data Why Is Big Data Important? WHY IS IMPORTANT? The importance of big data doesn’t simply revolve around how much data you have. The value lies in how you use it. By taking data from any source and analyzing it, you can find answers for: 1) streamline resource management, 2) improve operational efficiencies, 3) optimize product development, 4) drive new revenue and growth opportunities and 5) enable smart decision making. When you combine big data with high-performance analytics, you can accomplish business-related tasks such as: High performance analytics a. Determining root causes of failures, issues and defects in near-real time. b. Spotting anomalies faster and more accurately than the human eye. c. Improving patient outcomes by rapidly converting medical image data into insights. d. Recalculating entire risk portfolios in minutes. e. Sharpening deep learning model's ability to accurately classify and react to changing variables. f. Detecting fraudulent behavior before it affects your organization. Applications APPLICATIONS 1. Banking and Securities 2. Communications, Media and Entertainment 3. Healthcare Providers 4. Education 5. Manufacturing and Natural Resources 6. Government 7. Insurance 8. Retail and Wholesale trade 9. Transportation 10. Energy and Utilities EXAMPLES Examples AMAZON: ADS BASED ON WHAT YOU BUY How it’s using big data: Like Facebook and Google, adtech’s “duopoly,” Amazon got sucked into the advertising business by the sheer amount of consumer data at its disposal. Since its founding in 1994, the company has collected reams of information on what millions of people buy, where those purchases are delivered and which credit cards they use. In recent years, Amazon has begun offering more and more companies — including marketing companies — access to its self-service ad portal, where they can buy ad campaigns and target them to ultra-specific demographics, including past purchasers. HERE TECHNOLOGIES: LIVE MAPS FOR SELF-DRIVING CARS How it’s using big data: The experts at HERE Technologies leverage location data in several ways, most notably in the HD Live Map, which feeds self-driving cars the layered, location-specific data they need. The map pinpoints lane boundaries and senses a car’s surroundings. Thanks to data from intelligent sensors, the map can see around corners in a way the human eye can’t. And a perpetual stream of intel from fleets of roaming vehicles helps the map warn drivers about lane closures miles away. Google Earth Engine TRY IT YOURSELF! EXAMPLE Please enter the following website: https://code.earthengine.google.com/# USE YOUR GOOGLE ACCOUNT! Google Earth Engine Code Editor You should see a similar platform! Analyzing forest loss based on remote sensing images - Hansen Global Forest Change The code var gfc2014 = ee.Image('UMD/hansen/global_forest_change_2015'); //Globális erdőállomány réteg beolvasása // Változók létrehozása az egyes rétegeknek var treeCover = gfc2014.select(['treecover2000']); // erdőborítás var lossImage = gfc2014.select(['loss']); // erdőborítás veszteség var gainImage = gfc2014.select(['gain']); // erdőborítás gyarapodás // Erdőborítás réteg hozzáadása zöld színnel Map.addLayer(treeCover.updateMask(treeCover), {palette: ['000000', '00FF00'], max: 100}, 'Forest Cover'); // Összes erdőállomány veszteség piros színnel Map.addLayer(lossImage.updateMask(lossImage), {palette: ['FF0000']}, 'Loss'); // Összes erdőállomány gyarapodás kék színnel Map.addLayer(gainImage.updateMask(gainImage), {palette: ['0000FF']}, 'Gain'); References REFERENCES 1. https://www.youtube.com/watch?v=bAyrObl7TYE&ab_channel=Simplilearn 2. https://www.simplilearn.com/tutorials/big-data-tutorial?source=sl_frs_nav_playlist_video_clicked#what_is_big_data 3. https://www.guru99.com/what-is-big-data.html?fbclid=IwAR0mmpPQylz_4eOozsURRUGWl4KcromB-1PmIZPmKnxmQOrH20yRyk5eodY 4. https://opensistemas.com/en/the-four-vs-of-big-data/ 5. https://www.sas.com/en_us/insights/big-data/what-is-big-data.html 6.

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