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In a real world, it is often in a group setting that sensitive information has to be stored in databases of a server. Although personal information does not need to be stored in a server, the secret information shared by group members is likely to be stored there. The shared sensitive information requires more security and privacy protection. To our best knowledge, there is no paper which deals with...
Social networks form an important platform for information sharing and interaction among users. The content from social networks can be used to generate recommendations for users in order to help them to choose what they desire. There exist a lot of recommendation methods currently. In this paper, we propose a keyword
In this demo paper, we present a new data service composition sequence generation approach to solve the ad-hoc data query problem in EDMIS. Our approach allows end users to input some keywords, and then the data services related are found and the Top-K data services composition sequences are generated as output.
on mining and ranking existing travel routes from check-in data. We observe that when planning a trip, users may have some keywords about preference on his/her trips. Moreover, a diverse set of travel routes is needed. To provide a diverse set of travel routes, we claim that more features of Places of Interests (POIs
connect any two keywords, (2) The eccentricity of keyword vertices, a well known path measure. Our analysis shows that K-H networks conform to the phenomenon of the shrinking world. Specifically, it shows that the number of vertices of any two keywords, that were not originally connected in the K-K networks, is exactly three
distances in a multidimensional scaling space. In this study, we introduce an example of a 3-D multimedia space using the Associated Keyword Space (ASKS) and demonstrate similarity relationships between various sources of data in this space.
interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.
This paper characterizes optimal keyword auction mechanism, obtains Revenue Equivalence Theorem for keyword auctions and designs a kind of optimal keyword auction mechanism by considering the variable costs of advertisement positions. Under assumptions that the valuation of advertisement position to the advertiser is
Real-time keywords potentially demonstrate positive effects when they are provided in cross-cultural communication. Previously real-time keywords generated by a speaker during talking were investigated, and it was found it contributes to build mutual understanding and knowledge. However the use of keywords was not
Choosing descriptive keywords to best describe digital media content is crucial for many applications, especially those involving content-based indexing or retrieval. Traditionally such keywords are selected manually, which is labor intensive, restrictive to a limited set of words and inherently subjective to the
social media. Discovering keyword-based correlated networks of these large graphs is an important primitive in data analysis, from which users can pay more attention about their concerned information in the large graph. In this paper, we propose and define the problem of keyword-based correlated network computation over a
We investigate the problem of processing a large amount of continuous spatial-keyword queries over streaming data, which is essential in many applications such as location-based recommendation and advertising, thanks to the proliferation of geo-equipped devices and the ensuing location-based social media applications
propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF
The use of Search Engine enables the information seeker to seek information from a wide range of categories. Cultural information is one of the unique categories among the classes of information searched by users. This is because, there exists a significant relationship between a cultural keyword and its' originating
Social media keeps growing and providing us with rich sources of information to understand our everyday lives, customs, and culture in the form of periodic topics. This paper proposes a method of detecting periodic topics based on autocorrelation using the time series of the document frequencies of keywords. To deal
monthly automobile sales using sentiment and topical keyword frequencies related to the target brand over time on social media. Our predictive model illustrates how different time scale-based predictors derived from sentiment and topical keyword frequencies can improve the prediction of the future sales.
solution helps in reducing the time to write documents by 42% as compared to the traditional methods of writing documents. Sophisticated statistical algorithms along with natural language processing technology are used to continuously determine the keywords and concepts from the content in the document. A web search is
YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and
Ever growing music collections ask for novel ways of organization. The traditional browsing of folder hierarchies or search by title and album tends to be insufficient to maintain an overview of a collection of orders of thousands of tracks. Methods based on song similarity offer an alternative to keyword-based search
part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
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