Design, implementation and validation of AI-inspired information systems

While there is an emerging and always-growing interest for novel paradigms appeared recently (e.g., social networks, Cloud computing, NoSQL databases, Big Data, and so forth), Artificial Intelligence (AI) always plays a critical role in next-generation Information Systems. Indeed, as technology and paradigms pervade our life, there is a challenging need for smarter and more sophisticated Information Systems, for instance using innovative methodologies like crowdsourcing. As a consequence, it is natural to foresee the advancement of a novel class of Information Systems, which we call as AI-Inspired Information Systems. Basically, these are Information Systems which incorporate in their critical layers (i.e., design, implementation, validation) AI methodologies, yet extending their roots to classical foundations, with, indeed, exciting innovations

A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments

Abstract Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains

Inverse Tree-OLAP: Definition, Complexity and First Solution

Count constraint is a data dependency that requires the results of given count operations on a relation to be within a certain range. By means of count constraints a new decisional problem, called the Inverse OLAP, has been recently introduced: given a flat fact table, does there exist an instance satisfying a set of given count constraints? This paper focus on a special case of Inverse OLAP, called Inverse Tree-OLAP, for which the flat fact table key is modeled by a Dimensional Fact Model (DFM) with a tree structure

Frequent subgraph mining from streams of linked graph structured data

Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked resources. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Hence, ecient knowledge discovery algorithms for mining frequent subgraphs from these dynamic, streaming graph structured data are in demand. Some existing algorithms require very large memory space to discover frequent subgraphs; some others discover collections of frequently co-occurring edges (which may be disjoint). In contrast, we propose|in this paper|algorithms that use limited memory space for discovering collections of frequently co-occurring connected edges. Evaluation results show the effectiveness of our algorithms in frequent subgraph mining from streams of linked graph structured data

Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks

Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematical functions, such as derivatives, convolutions, and matrix inversions and transpositions. Such operations demand higher processing power than what is typically required for regular computer usage. Since CPUs are built with serial processing, they are not appropriate for handling large image datasets. On the other hand, GPUs have parallel processing capabilities and can provide higher speed. This paper utilizes advanced neural network techniques, such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST VGG16, and our proposed models, to compare CPU and GPU resources. We implemented a system for classifying Autism disease using face images of autistic and non-autistic children to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It was observed that GPU outperformed CPU in all tests conducted. Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU

Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki

Data analytics on the board game Go for the discovery of interesting sequences of moves in josek

Approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Knowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data

Capturing and Scaling Up Concurrent Transactions in Uncertain Databases

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-42017-7_6[EN] This chapter provides a framework for capturing and scaling up concurrent transactions in uncertain databases. Models and methods proposed in the context of this framework for managing data uncertainty are innovative as previous studies have not considered the specific case of concurrent transactions, which may worsen the uncertainty of database management activities beyond the simplest case of isolated transactions. Indeed, as this chapter demonstrates, inconsistency tolerance of integrity management, constraint checking and repairing easily scale up to concurrent transactions in a natural way, and query answers in concurrent transactions over uncertain data remain certain in the presence of uncertainty. This analytical contribution is enriched by means of a reference architecture for uncertain database management under concurrent transactions that strictly adheres to models and methods that are the main contributions of this research.The second and the third author have been supported by FEDER and the Spanish grants TIN2009-14460-C03, TIN2010-17139.Cuzzocrea, A.; Decker, H.; Muñoz-Escoí, FD. (2013). Capturing and Scaling Up Concurrent Transactions in Uncertain Databases. Communications in Computer and Information Science. 246:70-85. https://doi.org/10.1007/978-3-642-42017-7_6S708524

Social Big Data: Mining, Applications, and Beyond

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