The following three articles are full versions of extended abstracts that were presented at the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) held in Paris, France, June 14–16, 2004. After the conference, the authors were invited to submit full versions of their papers for publication in JACM. The articles have been reviewed according to the standard JACM refereeing process. We wish to thank the PODS Program Committee for their help in selecting these papers.

Journal of the ACM

Call for papers for Special Issue of ACM Transactions on Multimedia Computing, Communications and Applications on Interactive Digital Television

ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP)

Mixed Reality can be a valuable tool for research and development in robotics. In this work, we refine the definition of Mixed Reality to accommodate seamless interaction between physical and virtual objects in any number of physical or virtual environments. In particular, we show that Mixed Reality can reduce the gap between simulation and implementation by enabling the prototyping of algorithms on a combination of physical and virtual objects, including robots, sensors, and humans. Robots can be enhanced with additional virtual capabilities, or can interact with humans without sharing physical space. We demonstrate Mixed Reality with three representative experiments, each of which highlights the advantages of our approach. We also provide a testbed for Mixed Reality with three different virtual robotics environments in combination with the Crazyflie 2.0 quadcopter.

Mixed reality for robotics

In recent years, virtual reality and augmented reality applications have seen a significant increase in popularity. This is due to multiple technology trends. First, the availability of new tethered and wireless head-mounted displays allows viewers to consume new types of content. Second, 360° omnidirectional cameras, in combination with production software, make it easier to produce personalized 360° videos. Third, beyond these new developments for creating and consuming such content, video sharing websites and social media platforms enable users to publish and view 360° video content. In this paper, we present challenges of 360° video streaming systems, give an overview of existing approaches for 360° video streaming, and outline research opportunities enabled by 360° video. We focus on the data model for 360° video and the different challenges and approaches of creating, distributing, and presenting 360° video content, including 360° video recording, storage, distribution, edge delivery, and quality-of-experience evaluation. In addition, we identify major research opportunities with respect to efficient storage, timely distribution, and cybersickness-free personalized viewing of 360° videos.

Scalable 360° Video Stream Delivery: Challenges, Solutions, and Opportunities

Quality assurance for measurements in chemistry and the biosciences seems to deal at a first look with questions such as ‘Did we determine the amount of the requested compound (acrylamide, human growth hormone, etc) accurately?’ Why should the stakeholders of this journal reflect on ‘What to measure?’ Remember, most of the measurements are performed in response to a customer demand (which could be also our own scientific curiosity) And quality is about satisfying the customer One may argue that the analyst in the measurement laboratory obtains the specifications for the ‘what to measure’ from his/her customer But according to my experience, even a simple-looking request from a nonmeasurement expert to the analyst that she/he should just perform a ‘routine service’ by analysing a provided sample with respect to pre-specified parameters can turn into a fruitful scientific discussion and collaboration about defining together the analytical problem and designing a measurement strategy which could really answer the original question By that we are focussing on quality, because we ensure the provision of measurement results allowing the customer to take an informed decision We are not just producing measurement data about a sample, which would comply with the stereotype of an analyst as ‘service maid’ to other scientists dealing with the ‘grand challenges’ of society Consequently, delivering measurement results of adequate quality includes also ‘measuring the right target’ Therefore, the first required step to quality is identifying what is the decision-relevant measurand which is— according to ISO Guide 99 (VIM-3)—the ‘quantity intended to be measured’ However, this may not be sufficient for specifying what to measure; as such an ‘intended quantity’ may not be directly accessible by a measurement This is, for example, the case for a measurand ‘mass fraction of total protein in a foodstuff’ Therefore, the next step requires the intellectual contribution of analysts (‘measurement scientists’) in the problem-solving exercise to an even greater extent Namely, one has to specify the (physical, chemical or biological) entity that can represent adequately the identity, sometimes called analyte or target species (here ‘total protein’), of the measurand (here mass fraction of total protein in a foodstuff) in the measurement procedure For example, the nitrogen content in the food sample as measured by using the so-called Kjeldahl method may serve as substituting quantity in the example But this has to be well thought-out throughout the whole measurement process For the latter, various operations from taking the analytical subsample via sample preparation, creating and registering the instrumental signal until delivering the quantitative measurement result have to be considered Otherwise, one may overlook the interference of nitrogen from non-protein sources, such as melamine, in the foodstuff Many chemical or biological measurement procedures include transformations of the analyte, for instance, by derivatisation or fragmentation Consequently, ‘measurement’ in chemistry and the biosciences can usually not be reduced to a simple one-step attribution of a value to the predefined measurand It includes also mastering the changes of identity (chemical nature) for the target species during the whole measurement procedure For instance, getting to know the mass fraction of acrylamide in toasted bread may include the following steps: extraction (accurate knowledge on extraction yield required), bromination (derivatisation yield has to be known), gas chromatography (separation yield has to be checked) and mass spectrometry Calibration H Emons (&) Geel, Belgium e-mail: JRC-IRMM-ACQUAL@eceuropaeu

What to measure

This paper presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system which is arguably the most popular and largest multimedia streaming p2p system today. We believe that our findings can be used to understand large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of channel population size and session length.Our studies also reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) Small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure, (2) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel population size, (3) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing users, PPLive peers are impatient, (5) Session lengths (discretized, per channel) are typically geometrically distributed, (6) Channel Population Size variations are larger than in p2p file-sharing networks, yet they can be fitted with polynomial mathematical models. We conclude with a series of suggestions on how our findings can improve IPTV future design.

Measurement and modeling of a large-scale overlay for multimedia streaming

 While several deployed p2p le sharing overlays have been characterized in the literature, this paper shows that some of their conclusions may be false for p2p applications that stream media instead. Speci cally, we undertake a crawler-based investigation of PPLive, the largest live multimedia streaming system in the world today. It is important to understand IPTV overlays like PPLive in order to enable the building of larger-scale media streaming overlays. Our task is challenging because PPLive is proprietary. PPLive has multiple channels, each channel with its own overlay, and a large fraction of these channels stream preset movie schedules. A human user may join any given channel, but the user's client machine could be used to relay feeds for other non-subscribed channels too. Popular PPLive channels contain several thousands of nodes. We crawl the real deployed PPLive network via both machines in a cluster at UIUC, and by using PlanetLab hosts. Our major ndings are: (1) Unlike p2p le sharing users, PPLive peers are impatient, (2) Channel Size variations are larger than in p2p le sharing networks, (3) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel size, (4) Smaller PPLive overlays are similar to random graphs in structure, (5) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation. We believe these results point us towards taking seriously the nature of applications while designing and optimizing p2p overlays.

https://www.ideals.illinois.edu/bitstream/handle/2142/11240/Mapping%20the%20PPLive%20Network%20Studying%20the%20Impacts%20of%20Media%20Streaming%20on%20P2P%20Overlays.pdf?sequence=2

Mapping the PPLive Network: Studying the Impacts of Media Streaming on P2P Overlays

Many Peer-to-peer (P2P) applications such as media broadcasting and content distribution require a high performance overlay structure in order to deliver satisfying quality of service (QoS). Previous approaches to building such overlays either involve non-scalable solutions such as global information and shared contact point, or rely on gossip style membership dissemination, which lacks QoS awareness. In this paper, we present a distributed membership service called RandPeer, which manages membership information on behalf of P2P applications, and allows peers to locate good neighbors based on their QoS characteristics. Using this service, P2P applications can easily construct their overlays in a scalable and QoS aware fashion. We have implemented RandPeer and experimented in both local and wide area environments. Our results show that (1) RandPeer is scalable and robust to highly dynamic P2P memberships; (2) RandPeer has good lookup performance, both in terms of response time and the randomness of peer selection. The latter improves load balancing and failure resilience of P2P applications; (3) when used to improve the performance of a mesh based P2P overlay, RandPeer achieves 10% improvement in just 2 protocol rounds, which is more than 5 times faster than pure random neighbor selections.

/pdf/randpeer-membership-management-for-qos-sensitive-peer-to-1npl85sihm.pdf

RandPeer: Membership Management for QoS Sensitive Peer-to-Peer Applications

This paper presents the management overlay network (MON) system that we are building and running on the PlanetLab testbed. MON is a distributed system designed to facilitate the management of large distributed applications. Toward this goal, MON builds on-demand overlay structures that allow users to execute instant management commands, such as query the current status of the application, or push software updates to all the nodes. The on-demand approach enables MON to be light-weight, requiring minimum amount of resources when no commands are executed. It also frees MON from complex failure repair mechanisms, since no overlay structure is maintained for a prolonged time. MON is currently running on more than 300 nodes on the Planet-Lab. Our initial experiments on the PlanetLab show that MON has good performance, both in terms of command response time and achieved bandwidth for software push.

/pdf/mon-on-demand-overlays-for-distributed-system-management-4qgk6ek298.pdf

MON: on-demand overlays for distributed system management

PPLive is currently the most well-known instance of an IPTV (Internet Protocol Television) application which stands out due to its increasing popularity. As of May 2006, PPLive had over 200 distinct online channels, a daily average of 400,000 aggregated users, and most of its channels had several thousands of users at their peaks. During the Chinese New Year 2006 event, a particular PPLive channel had over 200,000 simultaneous viewers [2]. There are several measurement studies about PPLive characteristics [1, 2, 5]. These existing studies tend to predominantly look at either network-centric metrics (e.g., video traffic, TCP connections, etc.), or at user-centric metrics (e.g., geographic distribution, user arrival and departure, user-perceived quality, channel population, etc.). Our crawler-based measurement studies are unique in focusing primarily on overlay-based characteristics, which lie somewhere in between the user-centric view and the network centric view. Our studies reveal that (1) node degree is independent of channel population size, and (2) small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure.

Measurement of a large-scale overlay for multimedia streaming

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jin Liang

Author Tools

Chat about Author