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Despite challenges relating to privacy concerns and organizational resistance, Big Data market investments continue to gain momentum throughout the globe. In 2016, Big Data market vendors will pocket over $46 Billion from hardware, software and professional services revenues. Big Data investments are further expected to grow at a CAGR of 12% over the next four years, eventually accounting for over $72 Billion by the end of 2020. Complete report The Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts spreads across 390 pages, analysing key vendors and 86 data tables and figures is available at http://www.marketreportshub.com/big-data-market-research-2016-2030-sns-telecom.html. The market is ripe for acquisitions of pure-play Big Data startups, as competition heats up between IT incumbents. Nearly every large scale IT vendor maintains a Big Data portfolio. At present, the market is largely dominated by hardware sales and professional services in terms of revenue. Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market. By the end of 2020, research expects Big Data software revenue to exceed hardware investments by over $7 Billion. The report includes 22 chapters; the first chapter mainly introduced the product basic information that includes Executive Summary, Topics Covered, Historical Revenue & Forecast Segmentation, Key Questions Answered, Key Findings, Methodology, Target Audience and Companies & Organizations Mentioned. The second chapter is an Overview of Big Data that includes What is Big Data?, Key Approaches to Big Data Processing , Key Characteristics of Big Data, Market Growth Drivers and Market Barriers . The third chapter based on What are Big Data Analytics?, The Importance of Analytics, Reactive vs. Proactive Analytics, Customer vs. Operational Analytics and Technology & Implementation Approaches: Grid Computing, In-Database Processing, In-Memory Analytics, Machine Learning & Data Mining, Predictive Analytics, NLP (Natural Language Processing), Text Analytics, Visual Analytics and Social Media, IT & Telco Network Analytics. Order a Copy of Report at http://www.marketreportshub.com/purchase?rpid=3965. The 4th to 17th Chapters is based on Big Data Automotive, Aerospace & Transportation, Banking & Securities, Defense & Intelligence, Education, Healthcare & Pharmaceutical, Smart Cities & Intelligent Buildings, Insurance, Manufacturing & Natural Resources, Web, Media & Entertainment, Public Safety & Homeland Security, Public Services, Retail, Wholesale & Hospitality, Telecommunications, Utilities & Energy and Others. The 18th Chapter is based on Big Data Industry Roadmap & Value Chain. 19th Chapter is on Standardization & Regulatory Initiatives. 20th Chapter is based on Market Analysis & Forecasts, 21st on Vendor Landscape and 22 Chapter is a Conclusion & Strategic Recommendations The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report. List of Companies Mentioned are 1010data, Accel Partners, Adobe, Alameda County Social Services Agency, AlchemyDB, Aldeasa, AnalyticsIQ, Antic Entertainment, Antivia, AOL, Apple, AppNexus, Arcplan, Ascendas, AT&T, Attivio, Automated Insights, AutoZone, Avvasi, AWS (Amazon Web Services), Axiata Group, Ayasdi, BMC Software, BMW, Board International, Boeing, Booz Allen Hamilton, Box, ClearStory Data, Cloudera, Coca-Cola, Comptel, Concur, Concurrent, Constant Contact, Dimensional Insight, Dollar General, Renault, ReNet Tecnologia, Rentrak, Revolution Analytics, RiteAid, RJMetrics, Robi Axiata, Roche, Royal Dutch Shell, Royal Navy, RSA Group, Sabre, Sailthru, Sain Engineering, Salesforce.com, Salient Management Company, Samsung, Simba Technologies, SiSense, Skyscanner, SmugMug, Snapdeal, Software AG, Sojo Studios, SolveDirect, Sony Corporation, Southern States Cooperative, SpagoBI Labs, Splice Machine, Zoomdata, Zucchetti, Zurich Insurance Group, Zynga and more. Explore other new reports I.T. & Telecommunication Market at http://www.marketreportshub.com/categories/i-t-telecommunication. Market Reports Hub is your one-stop online shop for syndicated industry research reports on 25+ categories and their sub-sectors. We bring to you to the latest in market research across multiple industries and geographies from leading research publishers across the globe.

Ghadiyaram D.,University of Texas at Austin | Bovik A.C.,University of Texas at Austin | Yeganeh H.,Avvasi | Kordasiewicz R.,Avvasi | Gallant M.,Avvasi
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 | Year: 2014

We have created a new mobile video database that models distortions caused by network impairments. In particular, we simulate stalling events and startup delays in over-the-top (OTT) mobile streaming videos. We describe the way we simulated diverse stalling events to create a corpus of distorted videos and the human study we conducted to obtain subjective scores. We also analyzed the ratings to understand the impact of several factors that influence the quality of experience (QoE). To the best of our knowledge, ours is the most comprehensive and diverse study on the effects of stalling events on QoE. We are making the database publicly available [1] in order to help advance state-of-the-art research on user-centric mobile network planning and management. © 2014 IEEE.

Yeganeh H.,Avvasi | Kordasiewicz R.,Avvasi | Gallant M.,Avvasi | Ghadiyaram D.,University of Texas at Austin | Bovik A.C.,University of Texas at Austin
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

The vast majority of today's internet video services are consumed over-the-top (OTT) via reliable streaming (HTTP via TCP), where the primary noticeable delivery-related impairments are startup delay and stalling. In this paper we introduce an objective model called the delivery quality score (DQS) model, to predict user's QoE in the presence of such impairments. We describe a large subjective study that we carried out to tune and validate this model. Our experiments demonstrate that the DQS model correlates highly with the subjective data and that it outperforms other emerging models. © 2014 IEEE.

A monitoring system includes a video network analyzer processes the packet data to generate network and media session data relating to the distribution of video content via the data distribution network in at least one media session, wherein the network and media session data includes at least one session metric, location data, protocol identification data, video encoding parameter data, and audio encoding parameter data. An analytics database stores the network and media session data for generation of report data.

A device includes a frame data analyzer that generates buffer increment data based on frame data sent from the media server to the media client and further based on acknowledgement data sent from the media client to the media server. A playback data generator generates playback data based on frame data buffer contents and further based on player state data. A frame buffer model generator generates a buffer fullness indicator and the frame data buffer contents, based on the buffer increment data and the playback data. A player state generator generates the player state data, based on the buffer fullness indicator and further based on media client data, media server data and player command data.

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