Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Message from the ICDE 2015 Program Committee and general chairs

Since its inception in 1984, the IEEE International Conference on Data Engineering (ICDE) has become a premier forum for the exchange and dissemination of data management research results among researchers, users, practitioners, and developers. Continuing this long-standing tradition, the 31st ICDE will be hosted this year in Seoul, South Korea, from April 13 to April 17, 2015. It is our great pleasure to welcome you to ICDE 2015 and to present its proceedings to you

Cultural Capital: Challenges to New York State’s Competitive Advantages in the Arts and Entertainment Industry

This is a report on the findings of the Cornell University ILR planning process conducted with support of a grant from the Alfred P. Sloan Foundation to investigate trends in the arts and entertainment industry in New York State and assess industry stakeholders’ needs and demand for industry studies and applied research. Building on a track record of research and technical assistance to arts and entertainment organizations, Cornell ILR moved toward a long-term goal of establishing an arts and entertainment research center by forging alliances with faculty from other schools and departments in the university and by establishing an advisory committee of key players in the industry. The outcome of this planning process is a research agenda designed to serve the priority needs and interests of the arts and entertainment industry in New York State