Web Video in Numbers - An Analysis of Web-Video Metadata

Web video is often used as a source of data in various fields of study. While specialized subsets of web video, mainly earmarked for dedicated purposes, are often analyzed in detail, there is little information available about the properties of web video as a whole. In this paper we present insights gained from the analysis of the metadata associated with more than 120 million videos harvested from two popular web video platforms, vimeo and YouTube, in 2016 and compare their properties with the ones found in commonly used video collections. This comparison has revealed that existing collections do not (or no longer) properly reflect the properties of web video "in the wild".Comment: Dataset available from http://download-dbis.dmi.unibas.ch/WWIN

The PS-Battles Dataset - an Image Collection for Image Manipulation Detection

The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PS-Battles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.Comment: The dataset introduced in this paper can be found on https://github.com/dbisUnibas/PS-Battle

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

ADAMpro: Database Support for Big Multimedia Retrieval

For supporting retrieval tasks within large multimedia collections, not only the sheer size of data but also the complexity of data and their associated metadata pose a challenge. Applications that have to deal with big multimedia collections need to manage the volume of data and to effectively and efficiently search within these data. When providing similarity search, a multimedia retrieval system has to consider the actual multimedia content, the corresponding structured metadata (e.g., content author, creation date, etc.) and—for providing similarity queries—the extracted low-level features stored as densely populated high-dimensional feature vectors. In this paper, we present ADAM pro , a combined database and information retrieval system that is particularly tailored to big multimedia collections. ADAM pro follows a modular architecture for storing structured metadata, as well as the extracted feature vectors and it provides various index structures, i.e., Locality-Sensitive Hashing, Spectral Hashing, and the VA-File, for a fast retrieval in the context of a similarity search. Since similarity queries are often long-running, ADAM pro supports progressive queries that provide the user with streaming result lists by returning (possibly imprecise) results as soon as they become available. We provide the results of an evaluation of ADAM pro on the basis of several collection sizes up to 50 million entries and feature vectors with different numbers of dimensions

The Long Tail of Web Video

Web Video continues to gain importance not only in many areas of computer science but in society in general. With the growth in numbers, both of videos, viewers, and views, there arise several technical challenges. In order to address them effectively, the properties of Web Video in general need to be known. There is however comparatively little analysis of these properties. In this paper, we present insights gained from the analysis of a data set containing the meta data of over 100 million videos from YouTube. We were able to confirm common wisdom about the relationship between video duration and user engagement and show the extreme long tail of the distribution of video views overall. Such data can be beneficial in making informed decisions regarding strategies for large scale video storage, delivery, processing and retrieval

Reliable distributed data stream management in mobile environments

The proliferation of sensor technology, especially in the context of embedded systems, has brought forward novel types of applications that make use of streams of continuously generated sensor data. Many applications like telemonitoring in healthcare or roadside traffic monitoring and control particularly require data stream management (DSM) to be provided in a distributed, yet reliable way. This is even more important when DSM applications are deployed in a failure-prone distributed setting including resource-limited mobile devices, for instance in applications which aim at remotely monitoring mobile patients. In this paper, we introduce a model for distributed and reliable DSM. The contribution of this paper is threefold. First, in analogy to the SQL isolation levels, we define levels of reliability and describe necessary consistency constraints for distributed DSM that specify the tolerated loss, delay, or re-ordering of data stream elements, respectively. Second, we use this model to design and analyze an algorithm for reliable distributed DSM, namely efficient coordinated operator checkpointing (ECOC). We show that ECOC provides lossless and delay-limited reliable data stream management and thus can be used in critical application domains such as healthcare, where the loss of data stream elements can not be tolerated. Third, we present detailed performance evaluations of the ECOC algorithm running on mobile, resource-limited devices. In particular, we can show that ECOC provides a high level of reliability while, at the same time, featuring good performance characteristics with moderate resource consumption

How replicated data management in the cloud can benefit from a data grid protocol - the Re:GRIDiT Approach

Cloud computing has recently received considerable attention both in industry and academia. Due to the great success of the first generation of Cloud-based services, providers have to deal with larger and larger volumes of data. Quality of service agreements with customers require data to be replicated across data centers in order to guarantee a high degree of availability. In this context, Cloud Data Management has to address several challenges, especially when replicated data are concurrently updated at different sites or when the system workload and the resources requested by clients change dynamically. Mostly independent from recent developments in Cloud Data Management, Data Grids have undergone a transition from pure file management with read only access to more powerful systems. In our recent work,we have developed the Re:GRIDiT protocol for managing data in the Grid which provides concurrent access to replicated data at different sites without any global component and supports the dynamic deployment of replicas. Since it is independent from the underlying Grid middleware, it can be seamlessly transferred to other environments like the Cloud.In this paper, we compare Data Management in the Grid and the Cloud, briefly introduce the Re:GRIDiT protocol and show its applicability for Cloud Data Management

Crowd-based Semantic Event Detection and Video Annotation for Sports Videos

Recent developments in sport analytics have heightened the interest in collecting data on the behavior of individuals and of the entire team in sports events. Rather than using dedicated sensors for recording the data, the detection of semantic events reflecting a team's behavior and the subsequent annotation of video data is nowadays mostly performed by paid experts. In this paper, we present an approach to generating such annotations by leveraging the wisdom of the crowd. We present the CrowdSport application that allows to collect data for soccer games. It presents crowd workers short video snippets of soccer matches and allows them to annotate these snippets with event information. Finally, the various annotations collected from the crowd are automatically disambiguated and integrated into a coherent data set. To improve the quality of the data entered, we have implemented a rating system that assigns each worker a trustworthiness score denoting the confidence towards newly entered data. Using the DBSCAN clustering algorithm and the confidence score, the integration ensures that the generated event labels are of high quality, despite of the heterogeneity of the participating workers. These annotations finally serve as a basis for a video retrieval system that allows users to search for video sequences on the basis of a graphical specification of team behavior or motion of the individual player. Our evaluations of the crowd-based semantic event detection and video annotation using the Microworkers platform have shown the effectiveness of the approach and have led to results that are in most cases close to the ground truth and can successfully be used for various retrieval tasks

Icarus: Towards a Multistore Database System

The last years have seen a vast diversification on the database market. In contrast to the "one-size-fits-all" paradigm according to which systems have been designed in the past, today's database management systems (DBMSs) are tuned for particular workloads. This has led to DBMSs optimized for high performance, high throughput read/write workload in online transaction processing (OLTP) and systems optimized for complex analytical queries (OLAP). However, this approach reaches a limit when systems have to deal with mixed workloads that are neither pure OLAP nor pure OLTP workloads. In such cases, polystores are increasingly gaining popularity. Rather than supporting one single database paradigm and addressing one particular workload, polystores encompass several DBMSs that store data in different schemas and allow to route requests at a per-query-level to the most appropriate system. In this paper, we introduce the polystore Icarus. In our evaluation based on a workload that combines OLTP and OLAP elements, We show that Icarus is able to speed-up queries up to a factor of 3 by properly routing queries to the best underlying DBMS

QuAD: A Quorum Protocol for Adaptive Data Management in the Cloud

More and more companies move their data to the Cloud which is able to cope with the high scalability and availability demands due to its pay-as-you-go cost model. For this, databases in the Cloud are distributed and replicated across different data centers. According to the CAP theorem, distributed data management is governed by a trade-off between consistency and availability. In addition, the stronger the provided consistency level, the higher is the generated coordination overhead and thus the impact on system performance. Nevertheless, many OLTP applications demand strong consistency and use ROWA(A) for replica synchronization. ROWA(A) protocols eagerly update all (or all available) replicas and thus generate a high overhead for update transactions. In contrast, quorum-based protocols consider only a subset of sites for eager commit. This reduces the overhead for update transactions at the cost of reads, as the latter also need to access several sites. Existing quorum-based protocols do not consider the load of sites when determining the quorums; hence, they are not able to adapt at run-time to load changes. In this paper, we present QuAD, an adaptive quorum-based replication protocol that constructs quorums by dynamically selecting the optimal quorum configuration w.r.t. load and network latency. Our evaluation of QuAD based on Amazon EC2 shows that it considerably outperforms both static quorum protocols and dynamic protocols that neglect site properties in the quorum construction process