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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.

Amplified detection of nucleic acid by G-quadruplex based hybridization chain reaction.

Dow opens Photovoltaics Films Application Lab in Shanghai.

Researchers discover molecular mechanisms of left-right asymmetric control in the sea urchin.

China mulls new rule on human genetic research.

China to phase out organ donation from executed criminals.

Charles River Laboratories to expand research models business in China.

Chinese Science Academy Chief urges seizing on new technological revolution.

BGI contributes genome sequencing and bioinformatics expertise.

Taiwan government to encourage formation of smaller biotech funds.

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.

Organizations should maintain their innovation trajectories by developing products, processes, marketing, and organizational methods to achieve and sustain competitive advantage. However, by itself, creating value through innovation is not enough for companies: transforming these innovations into firm performance is also crucial. This study aims to validate the relationships among innovation and firm performance components and to explore the effect of innovation culture on innovation components and personnel performance. In our model, the innovation construct is comprised of innovation input, innovation process, and innovation output components, while firm performance construct includes four performance components such as financial, customer, market, and personnel performance. Moreover, this comprehensive model was proposed based on the literature, and structural equation modeling (SEM) was performed by employing data gained from 353 companies in Turkey to validate the model. According to the results, there is a sequential relationship within innovation components and firm performance components, while the relationships among innovation components and firm performance components are observed holistically. This paper contributes to the innovation literature by introducing a validated model to clarify these relationships. This model can be evaluated by company leaders to identify not only their firm’s innovation path but also short and long-term innovation results. Furthermore, the findings indicate that companies should manage the system from innovation input to financial gains without delicately compromising the whole sequential and holistic relationship. Managers should also be aware of the power of innovation culture on innovation path and personnel performance directly to create a convenient atmosphere.

The contribution of the paper is the building of a model to predict the proper employees’ allocation in the Greek public sector. To acknowledge the set and weights of criteria upon which our model feeds data, a validated questionnaire was developed and used to conduct a primary quantitative survey amongst HR departments and employees of state organizations in Greece. On the acquired findings, several experiments were administered using linear and machine learning tools, aiming to replace time consuming and subjective procedures, followed by many organizations. Concluding, data classification algorithms are proposed to predict the best matching of employees, giving as inputs personnel qualifications as well as job specifications, leading to a model based on J48, a decision tree algorithm.

Many studies in new product development (NPD) single out the use of information (especially market information) as a key predictor of NPD performance, but knowledge is lacking about what type of information is needed in each phase of the NDP process to enable high NPD performance. Based on a literature review and a pilot case study, this article increases the understanding of managing information in NPD. It is argued that the capability of managing information consists of three components: acquiring, sharing, and using information. By focusing on three different phases of the NPD process, 11 propositions regarding which information, information sources and means of cross-functional integration patterns that are most important to high NPD performance have been derived in each respective phase. In addition, the article also discusses antecedents and consequences of managing information. The article concludes with implications for managers, identifies limitations and proposes an agenda for further research into this area.

Digital disruptions are substantially impacting businesses and reshaping our economy worldwide, attracting increasing attention in research and practice. However, research lacks theoretical framing and understanding of the emergence, development, and impact of digital disruption. This study analyses and structures the fragmented knowledge on digital disruption by means of a systematic literature review, identifying five relevant key dimensions, i.e., disruption characteristics, market factors, organisational factors, value constellation, and impact/outcomes. Based on this analysis and classic disruption theory, we develop a theory-informed integrative framework, proposing nine relevant layers of digital disruption and deriving corresponding theoretical propositions for future research. The study makes an initial contribution towards theory development and a comprehensive understanding of the digital disruption concept. It may serve as the starting point for further theory development and a guiding scheme for managers on how to create or deal with digital disruption.

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.

With the rapid development of information technology and the wide application of electronic devices, the electromagnetic environment becomes more complex, external electromagnetic pulse may interfere with the electronic device at any time, affecting its normal work. Therefore, the electromagnetic pulse effect evaluation of electronic equipment is particularly important. In this paper, the complex electronic equipment system is like a “black box”. It introduces the minimum phase method, regarding the electronic equipment systems to satisfy the condition of minimum phase signal transmission system. Its system impulse response can be obtained by measuring its input and output signals, and then to predict the electromagnetic pulse waveform of the equipment. In the experiment, the effect of the predicted waveform is verified.