Application of Data Warehouse and Data Mining in Construction Management

All construction project are constrained by their schedules, budgets and specifications, and safety and environmental regulations. These constraints made construction management more complex and difficult. At the same time, many historical data that can support the decisions in the future are kept in construction enterprises,. To use the historical data effectively and efficiently, it is essential to apply the data warehouse and data mining technologies. This paper introduces a research which aims to develop a data warehouse system according to the requirements of construction enterprises and use data mining technology to learn useful information and knowledge from the data warehouse system. The design, the development and the application of this system are detailedly introduced in this paper

Halving on Binary Edwards Curves

Edwards curves have attracted great interest for their efficient addition and doubling formulas. Furthermore, the addition formulas are strongly unified or even complete, i.e., work without change for all inputs. In this paper, we propose the first halving algorithm on binary Edwards curves, which can be used for scalar multiplication. We present a point halving algorithm on binary Edwards curves in case of $d_1\neq d_2$. The halving algorithm costs about $3I+5M+4S$, which is slower than the doubling one. We also give a theorem to prove that the binary Edwards curves have no minimal two-torsion in case of $d_1= d_2$, and we briefly explain how to achieve the point halving algorithm using an improved algorithm in this case. Finally, we apply our halving algorithm in scalar multiplication with $\omega$-coordinate using Montgomery ladder

Content Analysis of Data Science Graduate Programs in the U.S.

Data science is an emerging academic field (Paul & Aithal, 2018), which has its origins in “Big Data/Cloud Computing” and complexity science domains. Data Science is about managing large and complex data (Big Data management) and analytics technologies (Paul & Aithal, 2018). Data, technology, and people are the three pillars of data science. In addition, Data Science is composed of three key areas: analytics, infrastructure, and data curation (Tang & Sae-Lim, 2016). Stanton (2012) defined data science as “an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information (Song & Zhu, 2016). Data science programs emphasize the implementation of tools, techniques, and visualization strategies, while data analytics programs emphasize the use of case studies and evolutions of tools (Murillo & Jones, 2019). Data science experts are needed in virtually every job sector, not just in technology. KDnuggest, a leading website on Big Data (Miller, 2020) reports that “Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.” In a study conducted by Bukhari (2020), a content analysis of the 30 Master’s Degree curricula in Data Science, revealed that schools that offer these programs are diverse: business, computer science, and science schools. On an average, Data Science master\u27s programs required 18.3 credits and 9.7 courses to complete the core requirements. However, there were inconsistencies in terms of the requirement across the 30 programs reviewed in this study. The objective of the study is to survey U.S. graduate programs in data science to understand the current situation of data science graduate education in the U.S.. The comparison of such program analyses with corresponding accreditation criteria will allow us to understand the stage of these programs, whether they are still in infancy or if they are on the path to maturity. A total of 422 graduate data science programs are analyzed in terms of their program profiles, including the degree names, department/school affiliation, geographic locations, types of universities (private vs. public). In addition, accreditation/guideline data from four accreditation agencies for graduate data science programs are analyzed. Corresponding accreditation analysis with all 422 programs will be reported. There are two major Implications from this study. On the one hand, findings from this study will provide an overview as well as a reference for any high education institutions to develop their own graduate data science program. On the other hand, practitioners in various industry or government segments will better understand the working force applying for Data Science jobs. It will start a dialogue between academia and industry partners to better prepare the Data Science work force

Is seeing really believing?: the role of video in remote communication between government agencies

Video-mediated communication (VMC) has been considered valuable for remote collaboration and conferencing. Particularly during military conflicts and national terrorism crises, various government agencies, including those in different countries, are commonly involved in remote collaborations. Trust is a critical factor in such remote collaborations. Video has been shown to be important in building trust in remote communication (Jun, et al, 2001; Rocco, 1998) and its desirability for that task has been assumed (Finn, Sellen, & Wilbur, 1997). However, recent studies show that video is unnecessary in most situations, except negotiation and communication with non-native speakers (Zhang, Olson & Olson, 2004, Short, William, & Christie, 1976; Veinott, Olson, Olson, & Fu, 1999). People communicate well enough with a pure voice connection (Gale, 1989; Ochsman & Chapanis, 1974; Williams, 1977). Clearly, to improve teamwork among crosscultural government agencies, it is important to understand how communication media (video, Instant Messaging, face-to-face) and culture interact. To investigate this research problem, a series of studies have been proposed that will attempt to analyze whether video communication confers significant benefits on trust perception and development among government agencies involved in global collaboration

Content Analysis of Two-year and Four-year Data Science Programs in the United States

Data has grown exponentially in the last decade, and this growth has resulted in vast challenges for both business and IT domains (Hassan & Liu, 2019). This growth has given rise to the Data Science field, which has also grown exponentially in the last few years (Hassan & Liu, 2019; Song & Zhu, 2016). The Data Science field has its origins in the statistics and mathematics domain (Cao, 2017b), but is now considered a multidisciplinary field (Aasheim et al., 2015). Data Science warrants knowledge of data analytics, programming, systems, applications, informatics, computing, communication, management, and sociology (Aasheim et al., 2015; Hassan & Liu, 2019; Murillo & Jones, 2019; Cao, 2017a; Tang & Sae-Lim, 2016). The main objective of Data Science is to manage large amounts of complex data and to solve Big Data challenges (Paul & Aithal, 2018) through the implementation of tools, techniques, and visualization strategies (Murillo & Jones, 2019). The rise in the Data Science field has increased the demand for skilled Data Science professionals. Data Scientists collect, prepare, analyze, visualize, manage, and preserve extensive collections of information (Song & Zhu, 2016). To prepare a generation of workers in the skills needed for the Data Science field, higher educational institutions must prepare students to support the Big Data movement and the new technologies developed as a result of this movement (Debnath, 2016; Song & Zhu, 2016). The focus of a Data Science program is to allow students to develop reasoning, analytical, and problem-solving skills needed to gather, process, decipher, and present data in a meaningful way (Debnath, 2016). Many universities are already offering Data Science programs (Song & Zhu, 2016). These programs vary widely in core courses and electives, with some concentrating more on the statistical and mathematical offerings while others on the computer and programming offerings. The purpose of this study is to conduct a content analysis of 136 two-year and four-year Data Science programs in order to acquire a deeper understanding of the undergraduate data science programs in the U.S. The program profile analysis includes; type of degrees, program names, department/school/college affiliations, type of institutions (private/public), and geographic locations. The study presents a comparative analysis of the accreditation criteria/guideline for Data Science programs established by four accreditation agencies and the results of the evaluation of all 136 Data Science programs for adherence to these criteria. The study provides a roadmap for institutions developing new Data Science programs or updating older programs into Data Science programs. Study findings will inform understanding of the breadth and width of undergraduate Data Science programs in the United States

Notes on Ghost Dark Energy

We study a phenomenological dark energy model which is rooted in the Veneziano ghost of QCD. In this dark energy model, the energy density of dark energy is proportional to Hubble parameter and the proportional coefficient is of the order $\Lambda^3_{QCD}$, where $\Lambda_{QCD}$ is the mass scale of QCD. The universe has a de Sitter phase at late time and begins to accelerate at redshift around $z_{acc}\sim0.6$. We also fit this model and give the constraints on model parameters, with current observational data including SnIa, BAO, CMB, BBN and Hubble parameter data. We find that the squared sound speed of the dark energy is negative, which may cause an instability. We also study the cosmological evolution of the dark energy with interaction with cold dark matter.Comment: 20 pages,10 figures,Correct some typos and add new reference

A comparative study on public-hosted blog sites in the U.S., China, and Korea

By early 2000s, blogging has become a global phenomenon in virtual communities and is rapidly emerging as a new form of computer-mediated communication (CMC). In the context of blogs, online trust can play an important role in connecting the author and readers. We identify that online interpersonal trust is reflected in the user profile of a blogger by how much personal information he or she reveals in it. In other words, more personal information a blogger reveals, more online trust he or she expresses towards the online community. In this paper, we explain our cross-cultural research in progress on blog and online trust. With three public blog hosting sites chosen from the U.S., China, and Korea, we first conducted a comparison analysis on blog hosting website interfaces to identify cultural characteristics of them. We then investigated levels of online trust of bloggers from three different cultures, by analyzing their willingness to reveal self-reported personal information in user profiles. Results show that bloggers in the U.S. reveal more information in their profile than ones in China or Korea, which indicates higher levels of interpersonal trust. In addition, we discovered that bloggers in Korea imply more cultural similarities to those in the U.S. than bloggers in China