Vodafone, Cyberpark ve Türkiye Teknoloji Geliştirme Vakfı işbirliğinde düzenlen etkinlikte büyük veri kavramı, Apache Hadoop Ekosistemi ve Türkiye ve Dünyadaki örnek uygulamalar anlatıldı.
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1 Haziran 2016 - Onur Karadeli, Mustafa Murat Sever
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
BigData HUB is a non-profit organization that help to spread Big Data and Data Science technology around Egyptian universities and Globally.
https://www.facebook.com/BigDataHub
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Big data nowadays is a new challenge to be managed, not as a barrier to grow up business. Data storages costs relatively is inexpensive, with more transactions generated from social media, machine, and sensors, data increased from pieces by pieces into pentabytes.
This slide explained what the challenges of Big Data (Volume, Velocity, and Variety) and give a solution how to managed them.
There are many tools that could help to solve the problems, but the main focus tools in this slide is Apache Hadoop.
BigData HUB is a non-profit organization that help to spread Big Data and Data Science technology around Egyptian universities and Globally.
https://www.facebook.com/BigDataHub
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
Big Data has been attested as one of the fastest growing technologies of this decade and thus potent enough to produce a large number of jobs. While enterprises across industrial stretch have started building teams, Hadoop technical interview questions could vary from simple definitions to critical case studies. Let’s take quick glimpse at the most obvious ones.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
General overview of the Big Data Concept.
Presentation of the Hierarchical Linear Subspace Indexing Method to perform exact similarity search in high dimensional data
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
عبارت کلان داده به مجموعههای داده ای اشاره دارد که به اندازه ای بزرگ و حجیم هستند که با ابزارهای مدیریتی و پایگاههاي داده سنتی و معمولی قابل مدیریت نیستند. مشکلات اصلی در کار با این نوع دادهها مربوط به برداشت و جمعآوری، ذخیرهسازی، جستوجو، اشتراکگذاری، تحلیل و نمایش آنها می باشد. کلان داده به عنوان یکی از فناوری های کلیدی و نوظهور به اذعان بسیاری از خبرگان می تواند تاثیرات شگرفی بر جای بگذارد. امروزه با گسترش شبکههای اجتماعی و ظهور منابع جدید اطلاعاتی، حجم دادههای تولیدی به شکل روزافزونی در حال افزایش است. نظرات کاربران شبکههای اجتماعی، محتواهای بههد اشتراک گذاشته شده و اطلاعات ضبط شده توسط حسگرهای مختلف همگی از انواع منابعی هستند که در این انفجار اطلاعاتی نقش ایفا می کنند. با استفاده از تحلیل حجمهاي بیشتری از دادهها، ميتوان تحلیلهاي بهتر و پيشرفتهتري را برای مقاصد مختلف، از جمله مقاصد تجاری، پزشکی و امنیتی، انجام داد و نتایج مناسبتری را دریافتکرد. پیوند موجود بین کلان داده و ابزارهای متن باز به وضوح با استفاده از ابزار هدوپ شروع شد و این روند در ادامه سرعت بیشتری به خود گرفت
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
Büyük Veri, Küme Hesaplama, Dağıtık Dosya Sistemi, Yüksek Performanslı Kümeleme, Apache Spark ve Streaming Modülünü içeren bir sunum.
Apache Spark’ın küme hesaplamaları için kullanımının anlatıldığı sunumda, Java API ile temel bir uygulama örneği gösteriliyor ve beraberinde gelen “Streaming Modülü” ile Twitter’dan canlı veri çekerek işlenmesi anlatılıyor.
10 Popular Hadoop Technical Interview QuestionsZaranTech LLC
Big Data has been attested as one of the fastest growing technologies of this decade and thus potent enough to produce a large number of jobs. While enterprises across industrial stretch have started building teams, Hadoop technical interview questions could vary from simple definitions to critical case studies. Let’s take quick glimpse at the most obvious ones.
Big Data Mining, Techniques, Handling Technologies and Some Related Issues: A...IJSRD
The Size of the data is increasing day by day with the using of social site. Big Data is a concept to manage and mine the large set of data. Today the concept of Big Data is widely used to mine the insight data of organization as well outside data. There are many techniques and technologies used in Big Data mining to extract the useful information from the distributed system. It is more powerful to extract the information compare with traditional data mining techniques. One of the most known technologies is Hadoop, used in Big Data mining. It takes many advantages over the traditional data mining technique but it has some issues like visualization technique, privacy etc.
I have collected information for the beginners to provide an overview of big data and hadoop which will help them to understand the basics and give them a Start-Up.
General overview of the Big Data Concept.
Presentation of the Hierarchical Linear Subspace Indexing Method to perform exact similarity search in high dimensional data
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
This is a power point presentation on Hadoop and Big Data. This covers the essential knowledge one should have when stepping into the world of Big Data.
This course is available on hadoop-skills.com for free!
This course builds a basic fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
• This course builds Understanding of Big Data problems with easy to understand examples and illustrations.
• History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop and was called Nutch
• What is Hadoop Magic which makes it so unique and powerful.
• Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
• And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
This course is available for free on hadoop-skills.com
Learn Big data and Hadoop online at Easylearning Guru. We are offer Instructor led online training and Life Time LMS (Learning Management System). Join Our Free Live Demo Classes of Big Data Hadoop .
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
عبارت کلان داده به مجموعههای داده ای اشاره دارد که به اندازه ای بزرگ و حجیم هستند که با ابزارهای مدیریتی و پایگاههاي داده سنتی و معمولی قابل مدیریت نیستند. مشکلات اصلی در کار با این نوع دادهها مربوط به برداشت و جمعآوری، ذخیرهسازی، جستوجو، اشتراکگذاری، تحلیل و نمایش آنها می باشد. کلان داده به عنوان یکی از فناوری های کلیدی و نوظهور به اذعان بسیاری از خبرگان می تواند تاثیرات شگرفی بر جای بگذارد. امروزه با گسترش شبکههای اجتماعی و ظهور منابع جدید اطلاعاتی، حجم دادههای تولیدی به شکل روزافزونی در حال افزایش است. نظرات کاربران شبکههای اجتماعی، محتواهای بههد اشتراک گذاشته شده و اطلاعات ضبط شده توسط حسگرهای مختلف همگی از انواع منابعی هستند که در این انفجار اطلاعاتی نقش ایفا می کنند. با استفاده از تحلیل حجمهاي بیشتری از دادهها، ميتوان تحلیلهاي بهتر و پيشرفتهتري را برای مقاصد مختلف، از جمله مقاصد تجاری، پزشکی و امنیتی، انجام داد و نتایج مناسبتری را دریافتکرد. پیوند موجود بین کلان داده و ابزارهای متن باز به وضوح با استفاده از ابزار هدوپ شروع شد و این روند در ادامه سرعت بیشتری به خود گرفت
Architectures for Data Commons (XLDB 15 Lightning Talk)Robert Grossman
These are the slides from a 5 minute Lightning Talk that I gave at XLDB 2015 on May 19, 2015 at Stanford. It is based in part on our experiences developing the NCI Genomic Data Commons (GDC).
Büyük Veri, Küme Hesaplama, Dağıtık Dosya Sistemi, Yüksek Performanslı Kümeleme, Apache Spark ve Streaming Modülünü içeren bir sunum.
Apache Spark’ın küme hesaplamaları için kullanımının anlatıldığı sunumda, Java API ile temel bir uygulama örneği gösteriliyor ve beraberinde gelen “Streaming Modülü” ile Twitter’dan canlı veri çekerek işlenmesi anlatılıyor.
Gelişen enformatik teknolojisinin olanak sağladığı veri depolama kapasitesinin konvansiyonel tekniklerle stratejik bilgiye dönüştürülemediği yaygın olarak paylaşılan bir gerçek. ASO Dergisi Aralık Sayısında yayınlanan bu çalışma dosyası matematiğin ve istatistiğin ötesinde bu verilerin bilgiye dönüştürülmesi ve risk yönetiminde kullanılması olanaklarını araştırıyor.
APT (Advanced Persistent Threat - Gelişmiş Devamlı Tehdit) saldırıları konusunda düzenlediğim seminere ait sunumun bir kısmıdır. Sunum içerisinde yer alan konu başlıkları buradadır, sunumun tamamını dağıtımını kontrol edebilmek ve içerisindeki hassas bilgilerin korunması amacıyla paylaşmıyorum.
15 Aralık 2016, Mef Üniversitesi Büyük Veri Analitiği yüksek lisans dersinde konuk konuşmacı olarak anlattığım IoT ve Data konusu. Microsoft'un bu konulardaki vizyonu, lokal örnekler ve başarılı kullanım senaryolarını konuştuğumuz slaytlar ekteki gibidir.
https://twitter.com/ikivanc
Günümüz dünyasında “performansın zaman metriği değişmiştir, aynı zamanda performans düzeyi de artmıştır”. Dolayısıyla gerçek zamanlı bir analizden söz ediliyorsa firmanın yarattığı gerçek değerin ölçülmesine ve görselleştirilmesine olanak sağlayacak Grafik DataMining tekniğine yoğunlaşmaları ve bunu öğrenmeleri gerekiyor. Bu bağlamda günümüz iş modelinin temel sorunu “hâlâ analitik dünyanın ölü diyagramlarına itibar ediliyor olmasıdır”. Yaşayan çok boyutlu işletmeleri kâğıt üzerindeki iki boyutlu ölü diyagramlara indirgemek faydadan çok zarara yol açmaktadır.
Gelişen enformatik teknolojisinin olanak sağladığı veri depolama kapasitesinin konvansiyonel tekniklerle stratejik bilgiye dönüştürülemediği yaygın olarak paylaşılan bir gerçek. ASO Dergisi 2015 Aralık Sayısında yayınlanan bu çalışma dosyası matematiğin ve istatistiğin ötesinde bu verilerin bilgiye dönüştürülmesi ve risk yönetiminde kullanılması olanaklarını araştırıyor.
Big Data yani büyük veri nedir diyorsanız ve büyük veri analizinin ne gibi yararlar sağlayacağını merak ediyorsanız sizin için Renerald olarak bu sunumu hazırladık. Büyük veri analizleri sayesinde, stratejilerinizi bilimsel veriler ışığında geliştirip şirketinize inanılmaz artı değerler kazandırabileceksiniz.
RECOVERY: Olay sonrası sistemleri düzeltmekAlper Başaran
Siber saldırıların sayısında görülen artış ve saldırganların beceri düzeyinde gözlemlenen iyileşme sonucunda kuruluşların bir siber güvenlik ihlali yaşama ihtimalleri artmaktadır.
Saldırıyı önlemeye odaklanan siber güvenlik yaklaşımının pek çok kuruluş için yetersiz kaldığını gözlemlediğimiz yüzlerce olay yaşandı ve yaşanmaya devam ediyor. Günümüz şartlarında bir kuruluşun siber güvenlik ihlali yaşaması halinde yapacaklarını bilmesi ve olay sonrası durumunu/sistemlerini düzeltmek için izleyeceği bir metodolojiye sahip olması çok önemlidir.
Bu webinarımızda yaşanması muhtemel bir siber güvenlik olayı sonrasında yapılması gerekenleri ve izlenmesi gereken yolu ele alacağız.
Webinarın amacı
Kuruluş bünyesinde, ağır veya hafif etkili, yaşanacak bir güvenlik ihlali sonrasında izlenebilecek bir yol haritası paylaşmak.
Kimler katılmalı
BT Birim çalışanları ve yöneticileri, risk birimi yöneticileri, SOME (Siber Olaylara Müdahale Ekibi) üyeleri
Slides used for the keynote at the even Big Data & Data Science http://eventos.citius.usc.es/bigdata/
Some slides are borrowed from random hadoop/big data presentations
Slides of my presentation at 9th Amirkabir Linux & Open-source Softwares Festival, about Big Data Computing Platforms and the rise of the so-called "Fast Data" phenomenon, and the architectures and state-of-the-art platforms for dealing with them.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
Automate your Data Science pipeline with Ansible, Python and Kubernetes - ODSC Talk
What is Data Science and the Data Science Landscape
Process and Flow
Understanding Data
The Data Science Toolkit
The Big Data Challenge
Cloud Computing Solutions
The rise of DevOps in Data Science
Automate your data pipeline with Ansible
Big Data Everywhere Chicago: Leading a Healthcare Company to the Big Data Pro...BigDataEverywhere
Mohammad Quraishi, Senior IT Principal, Cigna
Like Moses seeing the Promised Land from afar, we knew the big data journey would be worth it, but we didn't know how hard it would be. In this talk, I'll delve into the details of our big data and analytics initiative at Cigna,
SQLSaturday #230 - Introduction to Microsoft Big Data (Part 1)Sascha Dittmann
In dieser Session stellen wir anhand eines praktischen Szenarios vor, wie konkrete Aufgabenstellungen mit HDInsight in der Praxis gelöst werden können:
- Grundlagen von HDInsight für Windows Server und Windows Azure
- Mit Windows Azure HDInsight arbeiten
- MapReduce-Jobs mit Javascript und .NET Code implementieren
Introduction to Big Data & Big Data 1.0 SystemPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop.
#CHEDTEB
www.chedteb.eu
A summary of DBpedia's History and a detailed analysis of challenges and solutions.
We show how the Linked Data Cloud evolved around DBpedia and also what problems we and other data projects encountered. We included a section on the new solutions that will lead DBpedia into a bright future.
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
Büyük Veride Dikkate Alınması Gereken 4 Sorun | Big Data: Four Problems to Co...ideaport
Günümüz dünyasında, tüm insanlığın 20 yüz yıl boyunca ürettiği veri miktarı kadar veri üretiliyor! Heyecan verici ve neredeyse bağımlılık yaratan bir Büyük Veri çağına giriyoruz. Peki, veriyi iş hayatında daha iyi karar verebilmek için “yeterince” kullanabiliyor muyuz?
Dr. Milo Jones, Büyük Veri’nin vaatlerini ve tehlikelerini, veri işleyen ilk kurumlar olan istihbarat bakış açısıyla keşfediyor. Bu keşifle birlikte ortaya çıkardığı 4 temel soruyla, Büyük Veri’nin özellikle iş insanları ve firmalar için ne anlama geldiğini cevaplamaya çalışıyor.
Kaynak Yönetimi | Sevda Kılıçalp (@Refresh 2017 by TTGV)ideaport
"Vakıflarda Finansal Sürdürülebilirlik için Kaynak Yönetimi"
TTGV tarafından düzenlenen Refresh 2017 toplantısı, "Teknoloji Finansmanı ve Finansman Modelleri" bölümü.
Etkin Sosyal Yatırım | Prof. Dilek Çetindamar (@Refresh 2017 by TTGV)ideaport
"Vakıflar için alternatif kaynak yaratmada sosyal yatırımın önemi"
TTGV tarafından düzenlenen Refresh 2017 toplantısı, "Teknoloji Finansmanı ve Finansman Modelleri" bölümü
‘’Yapay zeka ve büyük veri saglık dünyasını nasıl degistirecek?, Saglıklı yasam trendi saglık harcamalarını azaltabilir mi? Laboratuvarda organ üretebilecek miyiz?, Saglıkta inovasyon neden önemli? Tüm bu soruların cevabı sezonun ilk ideaport etkinliği olan "Mitolojiden Geleceğe Sağlığın Yolcuğu" seminerinde B-WISE'ın kurucu ortağı Dr. Cenk Tezcan tarafından cevaplandı.
Making Blockchain Real for Business - Kathryn Harrison (IBM, Middle East and ...ideaport
Making Blockchain Real for Business - Kathryn Harrison (IBM, Middle East and Africa Payment and Blockchain Leader)
İçinde bulunduğumuz teknoloji çağının getirdikleri ile birlikte finans dünyasını yeni teknolojiler bekliyor. Bitcoin ve Blockchain teknolojisi bunların başında yer alıyor. Finans dünyasındaki tüm kuralları değiştirebilecek potansiyeli içinde barındıran bu iki teknolojiye olan ilgi her geçen gün artmakta. İstanbul Finans Derneği işbirliği ve Business Ankara medya sponsorluğunda düzenlenen etkinlikte, 'bitcoin' ve 'blockchain' teknolojileri gerek yazılım gerekse finansal boyutuyla ele alındı.
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31 Mart 2016
meet@ideaport | Finans Dünyasında Yeni Trend: Bitcoin ve Blockchain
Globalization for Turkish ICT Businesses - David Brown (151 Advisors)ideaport
Teknoloji firmaları küresel pazarlara nasıl açılır? Küresel bir firma olmanın aşamaları ve olası zorluklar. Küresel pazara açılırken yapılması ve yapılmaması gerekenler. İngiltere ve ABD'deki ICT pazarlarının özellikleri ve fırsatları. Ve 151 Advisors'ın bir firmanın küreselleşmesi için verdiği hizmetler...
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1 Haziran 2016
meet@ideaport | Teknoloji Firmalarının Globalizasyonu: Fırsatlar ve Zorluklar
Tüketicinin Kalbine Giden Yol Beyninden Geçer; Nöropazarlama - Seda Genç (Neu...ideaport
Özellikle son 5 senedir gündeme gelen bir kavram olan "nöro-pazarlama", bizi, giderek karmaşık bir hal almaya başlayan tüketici davranışları dünyasını daha iyi tanımaya ve anlamaya yönlendiriyor. Geleneksel pazarlama yöntemlerini tamamlayıcı nitelikte olmakla birlikte, marka sadakati yaratma, tüketici motivasyonunu artırma gibi pazarlama iletişimi aktivitelerini iyileştirmedeki etkisi yadsınamaz.
Ulusal ve uluslarası bir çok firmada nöropazarlama üzerine deneyim kazanan ve 2015 yılından Neuro-Mar firmasını yürüten Seda Genç değişen tüketiciyi anlamayı ve nöropsikolojiyi anlatıyor.
Teknoloji Sektöründe Fırsatlar - Baran Korkutideaport
“Teknolojik Girişimlerin İş Modeli Geliştirme Süreçlerinde Stratejik Düşünme”
10 yılı aşkın süredir dünyanın çeşitli yerlerinde, özellikle Telekom ve üretim sektörlerinde, deneyim sahibi olan Baran Korkut, bir tekno-girişimin doğru strateji ve iş modeli geliştirme süreçlerindeki anahtar noktaları anlattı.
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meet@ideaport | Teknolojik Girişimlerin İş Modeli Geliştirme Süreçlerinde Stratejik Düşünme
Leading through innovation - Balvinder Powarideaport
IE Business School Profesörü ve BOOSTER Uzay Endüstrileri Direktörü Balvinder Singh Powar iş ve özel hayatta başarı için inovasyon önemini anlattı.
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23 Mart 2016
meet@ideaport | Leading Through Innovation: Attitude for Success!
Enerji Sektöründe Endüstriyel IoT Uygulamaları - Şahin Çağlayan (Reengen)ideaport
Reengen Enerji IoT Platformu kurucu ortağı ve AR-GE sorumlusu Sahin Çaglayan, nesnelerin interneti ve büyük veri analizi yeteneklerini bir araya getirerek ticari binalarda ve enerji şebekesinde bulut tabanlı optimizasyon süreçlerini anlattı.
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23 Mart 2016
meet@ideaport | IoTxTR#21 'Enerji Sektöründe Endüstriyel IoT Uygulamaları' Semineri
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
5. Big Data is growing (Google Trends)
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6. Definition of Big Data
Big data is a term for data sets that are so large or complex
that traditional data processing applications are inadequate.
Challenges include analysis, capture, data curation, search,
sharing, storage, transfer, visualization, querying and
information privacy.
-BigData WIKIPEDIA
What is Big ?
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7. The ‘3V’ s
• Volume
• Velocity
• Variety
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8. Volume
• %40 Growth per year
• 50 Zettabytes by 2020
Ref:Where-is-your-data-FINAL-5a
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32. Apache Hadoop
• Open-Source Projects/Sub-projects of
Apache.
• Core projects
HDFS: Hadoop Distributed File System
MapReduce: Distributed Data processing
...
• Hadoop is not a database.
• Move computation to data !
• Now- %32 percent of all enterprise uses
Apache Hadoop.
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33. Apache Hadoop History
• 2003 Google File system paper
• 2006 Hadoop subproject created
• 2008 Sort record: Running on a 910-node cluster, Hadoop sorted one
terabyte in 209 seconds
• 2009 Yahoo runs 17 clusters with 24,000 machines
• 2011 Facebook, LinkedIn, eBay and IBM collectively contribute 200,000
lines of code
Ref: https://en.wikipedia.org/wiki/Apache_Hadoop
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34. Apache Hadoop Base Components & Enablers
Ref: http://synerzip.com - Innovation – It’s in our DNA
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35. BI & Visualization example
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Ref: http://forums.bsdinsight.com/articles/?page=4
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41. The Best Big Data Team should have ...
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• Data Hygienists – for clean data
• Data Explorers – discover data to use
• Business Solution Architects – combine data for a use case
• Data Scientists – for the right model
• Campaign Expert – for the best benefit
* From HBR : https://hbr.org/2013/07/five-roles-you-need-on-your-bi
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