Search

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value.

Abstract Background Genome browsers are widely used for locating interesting genomic regions, but their interactive use is obviously limited to inspecting short genomic portions. An ideal interaction is to provide patterns of regions on the browser, and then extract other genomic regions over the whole genome where such patterns occur, ranked by similarity. Results We developed SimSearch, an optimized pattern-search method and an open source plugin for the Integrated Genome Browser (IGB), to find genomic region sets that are similar to a given region pattern. It provides efficient visual genome-wide analytics computation in large datasets; the plugin supports intuitive user interactions for selecting an interesting pattern on IGB tracks and visualizing the computed occurrences of similar patterns along the entire genome. SimSearch also includes functions for the annotation and enrichment of results, and is enhanced with a Quickload repository including numerous epigenomic feature datasets from ENCODE and Roadmap Epigenomics. The paper also includes some use cases to show multiple genome-wide analyses of biological interest, which can be easily performed by taking advantage of the presented approach. Conclusions The novel SimSearch method provides innovative support for effective genome-wide pattern search and visualization; its relevance and practical usefulness is demonstrated through a number of significant use cases of biological interest. The SimSearch IGB plugin, documentation, and code are freely available at https://deib-geco.github.io/simsearch-app/ and https://github.com/DEIB-GECO/simsearch-app/ .

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.