The increase in the number of large-scale events held indoors (i.e., conferences and business events) opens new opportunities for crowd monitoring and access controlling as a way to prevent risks and provide further i...
详细信息
The increase in the number of large-scale events held indoors (i.e., conferences and business events) opens new opportunities for crowd monitoring and access controlling as a way to prevent risks and provide further information about the event's development. In addition, the availability of already connectable devices among attendees allows to perform non-intrusive positioning during the event, without the need of specific tracking devices. We present an algorithm for overcrowding detection based on passive Wi-Fi requests capture and a platform for event monitoring that integrates this algorithm. The platform offers access control management, attendees monitoring and the analysis and visualization of the captured information, using a scalable software architecture. In this paper, we evaluate the algorithm in two ways: First, we test its accuracy with data captured in a real event, and then we analyze the scalability of the code in a multi-core Apache Spark-based environment. The experiments show that the algorithm provides accurate results with the captured data, and that the code scales properly.
dataprocessing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models. We i...
详细信息
dataprocessing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models. We illustrate here a proposal where a high-level language, EXL, is used for the declarative specification of statistical programs, and a translation into executable form in various target systems is available. The language is based on the theory of schema mappings, in particular those defined by a specific class of tgds, which we actually use to optimize user programs and facilitate the translation towards several target systems. The characteristics of such class guarantee good tractability properties and the applicability in Big data settings. A concrete implementation, EXLEngine, has been carried out and is currently used at the Bank of Italy.
Everyday, a majority of the people, most probably several times, use the banking applications through online applications or physical ATM (Automated Teller Machine) devices for managing their financial transactions. H...
详细信息
Everyday, a majority of the people, most probably several times, use the banking applications through online applications or physical ATM (Automated Teller Machine) devices for managing their financial transactions. However, most financial institutions provide static user interfaces regardless of the needs for different customers. Saving even a few seconds for each transaction through more personalized interface design might not only result in higher efficiency, but also result in customer satisfaction and increased market share among the competitors. In ATM Graphical User Interface (GUI) design, transaction completion time is, arguably, one of the most important metrics to quantify customer satisfaction. Optimizing GUI menu structures has been pursued and many heuristic techniques for this purpose are present. However, menu optimization by employing an exact mathematical optimization framework has never been performed in the literature. We cast the ATM menu optimization problem as a Mixed Integer Programming (MIP) framework. All the parameters of the MIP framework are derived from a comprehensive actual ATM menu usage database. We also proposed two heuristic approaches to reduce the computational complexity. Our solution can be accustomed with ergonomic factors and can easily be tailored for optimization of various menu design problems. Performance evaluations of our solutions by using actual ATM data reveal the superior performance of our optimization solution.
The prevalence and the rapid growth of interconnected data have sparked the rise of graph models and systems focusing on the management of large graphs now available both in research and industry. The property graph m...
详细信息
ISBN:
(数字)9783031610035
ISBN:
(纸本)9783031610028;9783031610035
The prevalence and the rapid growth of interconnected data have sparked the rise of graph models and systems focusing on the management of large graphs now available both in research and industry. The property graph model allows the representation of information through multigraphs where nodes and edges can have labels and properties (i.e., key-value pairs). The model is becoming very popular and widespread, however related data management technology still faces many challenges, limiting the wide adoption of the model. In this vision paper, we present directions for future work in the domain focusing on the availability of a single declarative graph language, data integration, and scalable data processing. In our view, these areas represent key challenges for advancing research and practical solutions in the domain.
Parallel dataflow systems like Apache Flink allow analysis of large datasets with iterative programs. However, allocating a cost-effective set of resources for such jobs is a difficult task as the resource utilization...
详细信息
ISBN:
(纸本)9781509014828
Parallel dataflow systems like Apache Flink allow analysis of large datasets with iterative programs. However, allocating a cost-effective set of resources for such jobs is a difficult task as the resource utilization depends on many factors such as dataset size, key value distributions, computational complexity of programs, and the underlying hardware. What's more, some of these factors are not well known before the execution. There are, for example, often no data statistics such as key value distributions available beforehand. For this reason, we propose to improve the resource utilization at runtime using the repetitive nature of iterative dataflow programs. Based on runtime statistics gathered in previous iterations, the resource allocation is adapted dynamically at the synchronization barriers between iterations. This approach has two advantages: First, at barriers detailed statistics can be available, even for parallelly executed task pipelines. Second, at barriers dataflows can be adapted without complex handling of intermediate task state. This paper presents a prototype integrated with Apache Flink and an evaluation on a cluster with 480 cores. One experiment shows a 57% reduction of the job runtime by allocating more resources for a shorter time, another experiment a release of up to 40% surplus resources without significantly extending the job runtime.
In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an indu...
详细信息
In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher dataprocessing needs than the previous ones.
暂无评论