Sigmod/Pods 2012 Preliminary Program
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2017 CRM Buyer's Guide & Directory
magazine’s Over 1,500 2017 CRM and Related Solution Buyer’s Vendors GUIDE & Vendor Directory ollowing up on past years’ observations about current breakout trends, this year I’ll go with artificial intelligence and machine learning as the twin trends capturing the imaginations of the media, CRM gurus, vendors, Fand end users in 2017. At our CRM Evolution Conference this past April, Brent Leary brought together a panel of experts from SAP, Oracle, Microsoft Dynamics, Lithium Technologies, and Salesforce Einstein to discuss the role of AI in shaping modern customer engagement. Certainly, AI is still in its infancy, but its promise for all manner of customer-facing technologies is already emerging in everything from marketing automation to customer service to the onslaught of connected devices delivered by the Internet of Things. In the following pages of CRM magazine’s Buyer’s Guide & Vendor Directory, you’ll find a comprehensive list of CRM solution vendors we have identified throughout our research. I encourage you to visit their listings online for more in-depth profiles of their offerings, including all the content our editorial staff has written about them and their solutions, with relevant links, video, case studies, white papers, and indexes to make your journey easier and more efficient. Plus, this year we offer a comprehensive directory of every company currently on our radar that touches the CRM space. Sections of this CRM Buyer’s Guide & Vendor Directory will be updated quarterly. Visit www.destinationCRM.com/BuyersGuide for more information. Updated September 1, 2017 2017 Annual Buyer’s Guide Index Analytics Big Data CRM Channel Management Cloud-based CRM Customer Service/ Call Centers Enterprise CRM Integration Marketing Automation Mobile Sales Automation SMB/Mid-market CRM Social CRM Aspect Software Inc. -
Emerging Technologies of Big Data
NCI 2TM: 2015 978-81-927230-9-9 Emerging Technologies of Big Data Mrs Vidyullata Shekhar Jadhav Computer Application V.P. Institute of Management Studies & Research, Sangli [email protected] Abstract: Suppose a sample database table student with information Big data is an all encompassing term for any collection of Roll_no Class Name data sets so large or complex that it becomes difficult to 1 MCA I Raj process them using traditional data processing applications. 2 MCA II Ram Big data has three key strategic and operational challenges 3 MCAI Geeta which include Information Strategy: need to tie together the 4 MCAII Datta power of information assets. Big data is causing enterprises Then in Column-oriented databases the data would be stored to find new ways to leverage information sources to drive like this:1, 2, 3, 4;MCA I,MCA II,MCAI,MCAIII; Raj,Ram, growth. There are number of emerging technologies that Geeta, Datta; are helping users cope with and handle Big Data in a cost- Advantages of column oriented technology are effective manner these includes Column oriented databases, • High performance on aggregation queries (like Map Reduce, Hadoop, Hive, Pig , Platfora COUNT, SUM, AVG, MIN, MAX) • Highly efficient data compression and/or Keyword- Big data, Column oriented databases, Map partitioning Reduce, Hadoop, Hive, Pig,Platfora • True scalability and fast data loading for Big Data • Accessible by many 3 rd party BI analytic tools I. INTRODUCTION • Fairly simple systems administration Big data is an all encompassing term for any collection of data sets so large or complex that it becomes difficult to B. -
The Big Data Market: 2015 – 2030 Opportunities, Challenges, Strategies, Industry Verticals & Forecasts
The Big Data Market: 2015 – 2030 Opportunities, Challenges, Strategies, Industry Verticals & Forecasts Table of Contents 1 Chapter 1: Introduction ................................................................................... 18 1.1 Executive Summary ....................................................................................................................................... 18 1.2 Topics Covered .............................................................................................................................................. 20 1.3 Historical Revenue & Forecast Segmentation ............................................................................................... 21 1.4 Key Questions Answered ............................................................................................................................... 23 1.5 Key Findings ................................................................................................................................................... 24 1.6 Methodology ................................................................................................................................................. 25 1.7 Target Audience ............................................................................................................................................ 26 1.8 Companies & Organizations Mentioned ....................................................................................................... 27 2 Chapter 2: An Overview of Big Data ................................................................ -
Conference Booklet
2 ACM SIGMOD International Conference on Management of Data & ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems SIGMOD/PODS 2012 May 20–24 2012 Scottsdale, Arizona, USA http://www.sigmod.org/2012/ Conference Program 3 Copyright © 2012 Editors: K. Selçuk Candan (Arizona State University), Yi Chen (Arizona State University), Maurizio Lenzerini (University of Rome La Sapienza), Luis Gravano (Columbia University), Michael Benedikt (University of Oxford), Ariel Fuxman (Microsoft Research), Markus Krötzsch (University of Oxford) Template: Yannis Velegrakis, Toronto 2004, Available for download at: http://db.disi.unitn.eu/pages/programBooklet Permission to use the template is granted, with the kind request to include the current notice in the booklet. 4 TABLE OF CONTENTS Table of Contents .................................................................................................................. 5 Welcome Messages .............................................................................................................. 7 SIGMOD Conference Organization ............................................................................... 11 PODS Conference Organization ..................................................................................... 15 Sponsors, Supporters, and Exhibitors ............................................................................ 19 Conference Venue .............................................................................................................. 22 Program at a Glance .......................................................................................................... -
Business Intelligence & Big Data
BUSINESS INTELLIGENCE & BIG DATA m&a market landscape q4 2018 All securities transactions are executed by 7M Securities, LLC, member SIPC / FINRA. TABLE OF CONTENTS 3 Forward 4 Top Service Providers 5 Technology Partners 9 Strategic Buyers 14 Recent 7MA Transactions 15 Conferences 16 Closing 16 Contact Information 17 Sources 2 forward Overview at ~12x EBITDA in the LTM period [1]. Although Analytics is a hot topic in M&A right now, but these two areas are well established, they are is sometimes confusing considering the word expected to grow considerably (CAGR of 9.1% “analytics” encompasses so many different from 2016-2023) [2]. variations. One way to segment analytics is as follows: descriptive, diagnostic, predictive, According to Gartner’s recent survey of more prescriptive. This report hones in on the world than 3,000 CIOs, analytics and BI were ranked of descriptive and diagnostic analysis, two of the as the top differentiating technology for their four main segments. In these categories of organizations [3]. Despite this growth and analytics lies business intelligence & big data, market demand, in our discussions with a which for this report’s purposes will be Tableau Manager we found one of the trends described as follows: they are seeing is a massive shortage of qualified consulting talent. Management teams Business Intelligence: are trying to increase capabilities by adding Visualization of business data points professionals with experience in big data and business intelligence, yet the talent pool isn’t big Big Data: enough to support these initiatives; rather, they Capturing and utilizing large sets of information are adding capabilities via acquisitions or using third-party service providers. -
Platform Leadership in Open Source Software
Platform Leadership in Open Source Software By Ken Chi Ho Wong Bachelor of Science, Computing Science, Simon Fraser University, 2005 SUBMITTED TO THE SYSTEM DESIGN AND MANAGEMENT PROGRAM IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN ENGINEERING AND MANAGEMENT at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2015 ©2015 Ken Wong. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature of Author: ___________________________________________________________ Ken Wong System Design and Management Program February 2015 Advised by: ___________________________________________________________ Michael Cusumano SMR Distinguished Professor of Management & Engineering Systems MIT Sloan School of Management Certified by: ___________________________________________________________ Patrick Hale Director, System Design and Management Program Massachusetts Institute of Technology Platform Leadership in Open Source Software This page is intentionally left blank. Platform Leadership in Open Source Software By Ken Chi Ho Wong Submitted to the System Design and Management Program on February 2015, in Partial Fulfillment of the Requirements for the degree of Master of Science in Engineering and Management. Abstract Industry platforms in the software sector are increasingly being developed in open source. Firms seeking to position themselves as platform leaders with such technologies must find ways of operating within the unique constraints of open source development. This thesis aims to understand those challenges by analyzing the Android and Hadoop ecosystems through an augmented version of Porter’s Five Forces framework proposed by Intel’s Andrew Grove. The analysis finds that platform contenders in open source behave differently depending on whether they focus on competing against alternative platforms or alternative providers of the same platform as rivals.