Array DBMS and Satellite Imagery: Towards Big Raster Data in the Cloud
Heaps are well-studied fundamental data structures, having myriads of applications, both theoretical and practical. We consider the problem of designing a heap with an “optimal” extract-min operation. Assuming an arbitrary linear ordering of keys, a heap with n elements typically takes O(log n) time to extract the min-imum. Extracting all elements faster is impossible as this would violate the Ω(n log n) bound for comparison-based sorting. It is known, however, that is takes only O(n + k log k) time to sort just k smallest elements out of n given, which prompts that there might be a faster heap, whose extract-min performance depends on the number of elements extracted so far. In this paper we show that is indeed the case. We present a version of heap that performs insert in O(1) time and takes only O(log ∗ n + log k) time to carry out the k-th extraction (where log ∗ denotes the iterated logarithm). All the above bounds are worst-case.
The 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2019) will take place in Larnaca (Cyprus), on 9th and 10th June 2019. SMAP 2019 will be organized with the support of the Cyprus University of Technology.
The Semantic and Social Media Adaptation and Personalization (SMAP) workshop is the evolution of the Semantic Media Adaptation and Personalization initiative, which was founded during the summer of 2006 in an effort to discuss the state of the art, recent advances and future perspectives for semantic media adaptation and personalization. However, as Social Media applications have substantially transformed the way organizations, communities, and individuals interact, we have the scopes of SMAP extended towards this new trend, seeking to bring together researchers from the social web as well as from the semantic web communities.
We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ∼0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.
This volume contains the refereed proceedings of the 6th International Conference on Analysis of Images, Social Networks, and Texts (AIST 2017)1. The previous conferences during 2012–2016 attracted a significant number of students, researchers, academics, and engineers working on interdisciplinary data analysis of images, texts, and social networks. The broad scope of AIST made it an event where researchers from different domains, such as image and text processing, exploiting various data analysis techniques, can meet and exchange ideas. We strongly believe that this may lead to cross fertilisation of ideas between researchers relying on modern data analysis machinery. Therefore, AIST brought together all kinds of applications of data mining and machine learning techniques. The conference allowed specialists from different fields to meet each other, present their work, and discuss both theoretical and practical aspects of their data analysis problems. Another important aim of the conference was to stimulate scientists and people from industry to benefit from the knowledge exchange and identify possible grounds for fruitful collaboration. The conference was held during July 27–29, 2017. The conference was organised in Moscow, the capital of Russia, on the campus of Moscow Polytechnic University. This year, the key topics of AIST were grouped into six tracks: 1. General topics of data analysis chaired by Sergei Kuznetsov (Higher School of Economics, Russia) and Amedeo Napoli (LORIA, France) 2. Natural language processing chaired by Natalia Loukachevitch (Lomonosov Moscow State University, Russia) and Alexander Panchenko (University of Hamburg, Germany) 3. Social network analysis chaired by Stanley Wasserman (Indiana University, USA) 4. Analysis of images and video chaired by Victor Lempitsky (Skolkovo Institute of Science and Technology, Russia) and Andrey Savchenko (Higher School of Economics, Russia) 5. Optimisation problems on graphs and network structures chaired by Panos Pardalos (University of Florida, USA) and Michael Khachay (IMM UB RAS and Ural Federal University, Russia) 6. Analysis of dynamic behaviour through event data chaired by Wil van der Aalst (Eindhoven University of Technology, The Netherlands) and Irina Lomazova (Higher School of Economics, Russia) One of the novelties this year was the introduction of a new specialised track on process mining (Track 6).