Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Industrie 4.0 Framework, Challenges and Perspectives

The world is increasingly in a global community. The rapid technological development of communication and information technologies allows the transmission of knowledge in real-time. In this context, it is imperative that the most developed countries are able to develop their own strategies to stimulate the industrial sector to keep up-to-date and being competitive in a dynamic and volatile global market so as to maintain its competitive capacities and by consequence, permits the maintenance of a pacific social state to meet the human and social needs of the nation. The path traced of competitiveness through technological differentiation in industrialization allows a wider and innovative field of research. Already we are facing a new phase of organization and industrial technology that begins to change the way we relate with the industry, society and the human interaction in the world of work in current standards. This Thesis, develop an analysis of Industrie 4.0 Framework, Challenges and Perspectives. Also, an analysis of German reality in facing to approach the future challenge in this theme, the competition expected to win in future global markets, points of domestic concerns felt in its industrial fabric household face this challenge and proposes recommendations for a more effective implementation of its own strategy. The methods of research consisted of a comprehensive review and strategically analysis of existing global literature on the topic, either directly or indirectly, in parallel with the analysis of questionnaires and data analysis performed by entities representing the industry at national and world global placement. The results found by this multilevel analysis, allowed concluding that this is a theme that is only in the beginning for construction the platform to engage the future Internet of Things in the industrial environment Industrie 4.0. This dissertation allows stimulate the need of achievements of more strategically and operational approach within the society itself as a whole to clarify the existing weaknesses in this area, so that the National Strategy can be implemented with effective approaches and planned actions for a direct training plan in a more efficiently path in education for the theme.

On the analysis of data emerging in non-linear and complex systemscomparison of two X-Ray prony spectra

New arguments proving that successive (repeated) measurements have a memory and actually remember each other are presented. The recognition of this peculiarity can change essentially the existing paradigm associated with conventional observation in behavior of different complex systems and lead towards the application of an intermediate model (IM). This IM can provide a very accurate fit of the measured data in terms of the Prony's decomposition. This decomposition, in turn, contains a small set of the fitting parameters relatively to the number of initial data points and allows comparing the measured data in cases where the “best fit” model based on some specific physical principles is absent. As an example, we consider two X-ray diffractometers (defined in paper as A- (“cheap”) and B- (“expensive”) that are used after their proper calibration for the measuring of the same substance (corundum a-Al2O3). The amplitude-frequency response (AFR) obtained in the frame of the Prony's decomposition can be used for comparison of the spectra recorded from (A) and (B) - X-ray diffractometers (XRDs) for calibration and other practical purposes. We prove also that the Fourier decomposition can be adapted to “ideal” experiment without memory while the Prony's decomposition corresponds to real measurement and can be fitted in the frame of the IM in this case. New statistical parameters describing the properties of experimental equipment (irrespective to their internal “filling”) are found. The suggested approach is rather general and can be used for calibration and comparison of different complex dynamical systems in practical purposes.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.