1,721,007 research outputs found
The Metamorphosis (of RAM3S)
Full text linkThe 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 (Realtime 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
Approximate and Probabilistic Methods
No full textThe metric search paradigm has been to this day successfully applied to several real-world problems, ranging from multimedia to data mining, from decision support to pattern recognition, to statistical and medical applications. Indeed, its simplicity makes it a perfect candidate for solving a variety of similarity problems arising in applications. The casual reader may wonder what prevents the metric space paradigm to become ubiquitously applicable to the ever-increasing range of applications that can benefit from it. The answer to this question is so dreadful that researchers have given it the hideous name of "curse of dimensionality" (an entry in this issue of the bulletin is devoted to this concept). In its essence, the curse of dimensionality says that, whenever the (intrinsic) dimensionality D of the metric space is high, an efficient solution to NN (nearest neighbor) queries is impossible, and only a sequential scan of the whole dataset could guarantee that the correct result is found. This behavior is basically due to the fact that the variance of the distances to the query object q vanishes with increasing values of D, so that all data objects have almost the same distance to q. In such scenarios, one may however argue that NN queries lose of significance, since any data object would have a distance to the query object comparable to the minimal one. On the other hand, in several real-world cases searching for the exact NN is still difficult, yet the distribution of distances exhibits a sufficiently high variance to make the problem worth solving. In such cases, it is also observed that locating the NN of a query point is, in itself, a relatively easy task, whose complexity indeed decreases with space dimensionality. As a matter of fact, the hard problem in high-D exact NN search is to determine how to stop, i.e., how to guarantee that the current result is the correct one. From this it follows that most of the time spent in an (exact) NN search is wasted time, during which little (or no) improvement is obtained
Multimedia, Similarity, and Preferences: Adding Flexibility to Your Information Needs
No full textStarting from the 90’s, it was easily recognized that commonly adopted search paradigms were not enough to deal with at-the-time emerging novel DB applications, in which the presence of multimedia data and high dimensionality were both key aspects. In this paper we survey the research activity of our group in the last 25 years, therefore going through issues such as indexing, approximate query processing, and support for preference queries, which are now quite well understood. In doing this we also consider the need to provide the users with simple but powerful tools, able to smooth the processes of query creation/customization and of result interpretation. We complete with a look to the novel issues that the “Big Data” era brings to us
Multiple Instance Classification in the Image Domain
No full textMultiple instance classification (MIC) is a kind of supervised learning, where data are represented as bags and each bag contains many instances. Training bags are given a label and the system tries to learn how to label unknown bags, without necessarily learning how to label individually each of their instances. In particular, we apply concepts drawn from MIC to the realm of content-based image retrieval, where images are described as bags of visual local descriptors. We introduce several classifiers, according to the different MIC paradigms, and evaluate them experimentally on a real-world dataset, comparing their accuracy and efficiency. © 2019, Springer Nature Switzerland AG
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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