Narrow Results

Data lakes are storage repositories that contain large amounts of data (big data) in its native format; encompassing structured, semi-structured or unstructured. Data lakes are open to a wide range of use cases, such as carrying out advanced analytics and extracting knowledge patterns. However, the sheer dumping of data into a data lake would only lead to a data swamp. To prevent such a situation, enterprises can adopt best practices, among which to manage data lake metadata. A growing body of research has focused on proposing metadata systems and models for data lakes with a special interest on model genericness. However, existing models fail to cover all aspects of a data lake, due to their static modeling approach. Besides, they do not fully cover essential features for an effective metadata management, namely governance, visibility and uniform treatment of data lake concepts. In this paper, we propose a dynamic modeling approach to meet these features, based on two main constructs: data lake concept and data lake relationship. We showcase our approach by Megale, a graph-based metadata system for NoSQL data lake exploration. We present a proof-of-concept implementation of Megale and we show its effectiveness and efficiency in exploring the data lake.

This paper introduces a new framework for the design of parallel algorithms that may be executed on multiprogrammed architectures with variable resources. These features, in combination with an implied ability to handle fault tolerance, facilitates environments such as the GRID. A new model, BSPGRID is presented, which exploits the bulk synchronous paradigm to allow existing algorithms to be easily adapted and used. It models computation, communication, external memory accesses (I/O) and synchronization. By combining the communication and I/O operations BSPGRID allows the easy design of portable algorithms while permitting them to execute on non-dedicated hardware and/or changing resources, which is typical for machines in a GRID. However, even with this degree of dynamicity, the model still offers a simple and tractable cost model. Each program runs in its own virtual BSPGRID machine. Its emulation on a real computer is demonstrated to show the practicality of the framework. A dense matrix multiplication algorithm and its emulation in a multiprogrammed environment is given as an example.

This paper introduces a discrete time model for time-variant delays and investigates the very nature of such delays. It is shown that a linear system-delay interface is a system theoretic necessity for the construction of composite linear systems with time-variant delays. Based on this analysis, two interfaces of particular importance are presented and used to obtain new, simple to check stability results for queue control systems. The relevance of the presented modeling and stability results on queue control systems to QoS control in modern communication networks is illustrated via several examples.

The main function of the on-load tap changing (OLTC) regulators consists of maintaining a constant voltage in order to feed critical loads despite the load changes or voltage changes in the ac mains. The traditional regulators are still used nowadays, but they present several disadvantages, like a slow response, which reaches from 100 ms to several seconds. These drawbacks can be overcome if the OLTC regulators would have shorter response time, commuting several times every cycle of the mains. There are two basic topologies for fast OLTC regulators. The first one consists of several taps and uses hard switching. The second one consists of two main switches commuting at high frequency, using soft-switching in order to reduce the power losses. The present topology is of the second type. This paper presents a mathematical model of the power stage of the proposed regulator. The model includes the parasitic resistances and the leakage inductances in order to obtain a better comprehension of the regulator operation. A parametric analysis has been done in order to observe the influence of the parasitic elements in the performance of the main parameters of the topology. The model is verified by experimental results obtained using a 500-W prototype.

In this paper, a software toolchain is presented for the fully automatic alignment of a 3D human face model. Beginning from a point cloud of a human head (previously segmented from its background), pose normalization is obtained using an innovative and purely geometrical approach. In order to solve the six degrees of freedom raised by this problem, we first exploit the human face's natural mirror symmetry; secondly, we analyze the frontal profile shape; and finally, we align the model's bounding box according to the position of the tip of the nose. The whole procedure is considered as a two-fold, multivariable optimization problem which is addressed by the use of multi-level, genetic algorithms and a greedy search stage, with the latter being compared against standard PCA. Experiments were conducted utilizing a GavabDB database and took into account proper preprocessing stages for noise filtering and head model reconstruction. Outcome results reveal strong validity in this approach, however, at the price of high computational complexity.

The noise of the current accumulator is analyzed. A model of time-delay-integration (TDI) CMOS image sensor is presented, which is used to analyze the noise performance. In this model, input signals are accumulated four times by the type of current and then converted to digital signals to accomplish the other accumulation by 32 times, i.e., 4 × 32 accumulation mode. The noise, which includes switch charge injection, sample noise and kT/C noise, is considered in this model. The major source of the noise and the relationship between noise and sample capacitance are evaluated through the model simulation. The results indicate that the total noise can be restrained by increasing sample capacitance. When the input signal is arranging from 0 μA to 100 μA, the accuracy of the current accumulator can be 11 bits by using 1 pF sample capacitor. The SNR of the output signal can be increased by 20.38 dB which is close to the ideal result. The circuit of the current accumulator based on the model is also proposed.

It is well known that every sequential element may become metastable when provided with marginal inputs, such as input transitions occurring too close or input voltage not reaching a defined HI or LO level. In this case the sequential element requires extra time to decide which digital output level to finally present, which is perceived as an output delay. The amount of this delay depends on how close the element’s state is to the balance point, at which the delay may, theoretically, become infinite. While metastability can be safely avoided within a closed timing domain, it cannot be completely ruled out at timing domain boundaries. Therefore it is important to quantify its effect. Traditionally this is done by means of a “mean time between upsets” (MTBU) which gives the expected interval between two metastable upsets. The latter is defined as the event of latching the still undecided output of one sequential element by a subsequent one. However, such a definition only makes sense in a time-safe environment like a synchronous design. In this paper we will extend the scope to so-called value-safe environments, in which a sequential element can safely finalize its decision, since the subsequent one waits for completion before capturing its output. Here metastability is not a matter of “failure” but a performance issue, and hence characterization by MTBU is not intuitive. Therefore we will put the focus on the delay aspect and derive a suitable model. This model extends existing approaches by also including the area of very weak metastability and thus providing complete coverage. We will show its validity through comparison with transistor-level simulation results for the most popular sequential elements in different implementations, point out its relation to the traditional MTBU model parameters, namely $\tau$ and $T_0$, and show how to use it for calculating the performance penalty in a value-safe environment.

In this work, a surface potential modeling approach has been proposed to model dual gate, bilayer graphene field effect transistor. The equivalent capacitive network of GFET has been improved considering the quantum capacitance effect for each layer and interlayer capacitances. Surface potentials of both layers are determined analytically from equivalent capacitive network. The explicit expression of drain to source current is established from drift-diffusion transport mechanism using the surface potentials of the layers. The drain current characteristics and transfer characteristics of the developed model shows good agreement with the experimental results in literatures. The small signal parameters of intrinsic graphene transistor i.e., output conductance ($g_{\mathrm{\text{ds}}}$), transconductance ($g_m$), gate to drain capacitance ($C_{\mathrm{\text{gd}}}$) and gate to source capacitance ($C_{\mathrm{\text{gs}}}$) have been derived and finally, the cut-off frequency is determined for the developed model. The model is compared with reported experimental data using Normalized Root Mean Square Error (NRMSE) metric and it shows less than $16\%$ NRMSE. A Verilog-A code has been developed for this model and a single ended frequency doubler has been designed in Cadence Design environment using this Verilog-A model.

To overcome the limitations of mobile devices in executing computing-intensive workloads, mobile edge computing emerged at the times required. It can effectively support computing intensive and delay critical applications executed by Internet of Things devices with limited computing power and energy constraints and becomes the key technology of next-generation networks. This research first determined the optimization framework of the mobile edge computing task unloading system, built a basic platform for mobile edge computing task unloading after system optimization, completed the drill transformation of intelligent algorithm based on the mobile edge computing task unloading algorithm of the optimized system, and studied its convergence and applicability. Finally, by comparing several different mobile edge computing tasks unloading models, Select a suitable mobile edge computing task unloading model, and complete the practical effect test. The results show that: (1) Compared with the traditional unloading mode, the optimized mobile edge computing task unloading system has higher work efficiency; (2) The whole process model of mobile edge computing task unloading completed in this study can reduce the task processing delay in the actual use process; (3) The unloading system with intelligent algorithms is more suitable for edge devices, providing a reference task unloading model for engineering practice.

In order to explore the application of IoT technology in robots and the promotion of IoT robot technology to the economy, by comparing traditional technology and IoT intelligent robot technology, this article combines it with economic development to analyze the promotion of IoT robot to economic development. Based on the ultra-wideband ranging method, this paper designs an ultra-wideband radio frequency positioning system and applies it to the robot’s positioning process. Moreover, this article combines the application of robots in the current social and economic development to construct the system structure, and conducts functional analysis with manufacturing robots and monitoring robots as the main body. After constructing an intelligent robot based on the Internet of Things technology, by comparing the traditional technology and the intelligent robot technology of the Internet of Things, this article combines it with economic development to analyze the promotion of IoT robot to economic development. From the analysis results of this article, it can be seen that the advancement of IoT robot technology can effectively promote economic development.

In this paper, we study the uncertain multiple attribute decision making problems with preference information on alternatives (UMADM-PIA, for short), in which the information on attribute weights is not precisely known, but value ranges can be obtained. A projection method is proposed for the UMADM-PIA. To reflect the decision maker's preference information, a projection model is established to determine the weights of attributes, and then to select the most desirable alternative(s). The method can reflect both the objective information and the decision maker's subjective preferences, and can also be performed on computer easily. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and practicality.

Construction processes planning and effective management are extremely important for success in construction business. Head of a design must be well experienced in initiating, planning, and executing of construction projects. Therefore, proper assessment of design projects' managers is a vital part of construction process. The paper deals with an effective methodology that might serve as a decision support aid in assessing project managers. Project managers' different characteristics are considered to be more or less important for the effective management of the project. Qualifying of managers is based on laws in force and sustainability of project management involving determination of attributes value and weights by applying analytic hierarchy process (AHP) and expert judgement methods. For managers' assessment and decision supporting is used additive ratio assessment method (ARAS). The model, presented in this study, shows that the three different methods combined (ARAS method aggregated together with the AHP method and the expert judgement method) is an effective tool for multiple criteria decision aiding. As a tool for the assessment of the developed model, was developed multiple criteria decision support system (MCDSS) weighting and assessment of ratios (WEAR) software. The solution results show that the created model, selected methods and MCDSS WEAR can be applied in practice as an effective decision aid.

A new model for the control of financial processes based on metric graphs is presented. Our motivation has its roots in the current interest in finding effective algorithms to detect and classify relations among elements of a social network. For example, the analysis of a set of companies working for a given public administration or other figures in which automatic fraud detection systems are needed. Given a set $\mathrm{\Omega }$ and a proximity function $\varphi :\mathrm{\Omega }\times \mathrm{\Omega }\to {ℝ}^{+}$, we define a new metric for $\mathrm{\Omega }$ by considering a path distance in $\mathrm{\Omega }$ that is considered as a graph. We analyze the properties of such a distance, and several procedures for defining the initial proximity matrix ${(\varphi (a,b))}_{(a,b)\in \mathrm{\Omega }\times \mathrm{\Omega }}$. Using this formalism, we state our main idea regarding fraud detection: financial fraud can be detected because it produces a meaningful local change of density in the metric space defined in this way.

As an important grain base in China, the Northeast plays an important role in agricultural production. This paper hopes to analyze the industrial transformation in the northeast, select a series of indicators by AHP, and consider the impact of local agricultural capital investment on the provincial level of agricultural industry transformation through the super-efficiency SBM-DEA model. The results show that although the technical efficiency of agricultural transformation in Northeast China is high, there is a problem of insufficient scale efficiency. The relatively small scale of investment has a greater negative impact on the development of agricultural transformation in the region.

Traffic accidents have become a more and more important factor to restrict the development of economy and affect the safety of human. Gray System quests for the inner relation through the original data, this is an approach to find out the rule of data through other data. Highway traffic accident forecasting model based on Gray System uses some original data, through theory of Gray System, processing the data and modeling GM (1,1). Through the validation of actual data, error of GM (1,1) is minor, it can be used in actual forecasting.

Analysis and application about large data of crop Breeding, was not pure was a kind of technology evolution, but changed to mining and analysis large data of crop breeding use multiple granularity, multi-level and multi-channel analysis model and tools. According to the existing situation, this paper from the perspective of data integration, tool integration, method integration, put forward an model, it definition and description data and tools in the process analysis and application about large data of crop breeding, and then, set up a basic agent model frame structure, reasonably and effectively integrate all kinds of data and tools, so as to realize intelligent coordination, reasonable scheduling, the purpose of further improve the joint study, analysis mining.