Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

In order to improve the accuracy and efficiency of the clustering mining algorithm, this paper focuses on the clustering mining algorithm for large data. Firstly, the traditional clustering mining algorithm is improved to improve the accuracy, and then the improved clustering algorithm is parallelized to improve the efficiency. In order to improve the accuracy of clustering, an incremental K-means clustering algorithm based on density is proposed on the basis of K-means algorithm. Firstly, the density of data points is calculated, and each basic cluster is composed of the center points whose density is not less than the given threshold and the points within the density range. Then, the basic cluster is merged according to the distance between the two cluster centers. Finally, the points that are not divided into any cluster are divided into the clusters nearest to them. In order to improve the efficiency of the algorithm and reduce the time complexity of the algorithm, the distributed database was used to simulate the shared memory space and parallelize the algorithm on the Hadoop platform of cloud computing. The simulation results show that the clustering accuracy of the proposed algorithm is higher than that of the other two algorithms by more than 10%.

Improved K-Means Clustering Algorithm for Big Data Mining under Hadoop Parallel Framework

High-utility pattern mining is an important sub-literature in the data mining literature. This literature discusses the discovery of useful pattern information from large databases by considering not only supports of patterns but also profits and quantities of items. This literature has the potential to be applied to various problems in the real world, so many methods for the improvement of the algorithm performance have been studied. Moreover, there have also been attempts to extend the flexibility of this literature. The traditional approaches in this literature considered the positive unit profits of items in a given database only. However, this literature can take extended flexibility into account by considering negative as well as positive unit profits of the items. In this paper, we suggest an efficient approach for mining high-utility patterns with negative unit profits. Moreover, the experimental performance tests, which are performed on various real and synthetic datasets in this paper, show that the proposed algorithm has a better performance than the state-of-the-art methods in this literature in terms of the runtime, memory usage, and scalability. 

EHMIN: Efficient approach of list based high-utility pattern mining with negative unit profits

Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.

Location-based social network recommendations with computational intelligence-based similarity computation and user check-in behavior

In an uncertain environment, the process of fuzzy cross-evaluation in data envelopment analysis (DEA) models is explained by different types of fuzzy data. In this paper, the fuzzy cross-efficiency matrix with different types of fuzzy data will be changed into a cross-evaluation similarity matrix by similarity theory of generalized trapezoidal fuzzy numbers. Based on the fuzzy information retrieval (FIR) method, a new ordered-geometric-mean averaging operator is defined to yield aggregation weights by the relevance between the self-evaluation and cross-evaluation of each decision-making unit (DMU). Furthermore, the common neighbor DMU selection algorithm is constructed to classify the DMUs. Then, the fuzzy logic rules are proposed to modify the aggregation results by combining the clustering results. Finally, this paper demonstrates the applicability of fuzzy cross-efficiency aggregation using the FIR method by taking an example of 12 suppliers from the semiconductor industry.

Two-Stage Fuzzy Cross-Efficiency Aggregation Model Using a Fuzzy Information Retrieval Method

Leveraging community-contributed information such as blogs, Global Positioning System, and images that are geographically tagged for suggestions on navigation with mobile based communication is becoming a research area. The travel recommender a system is based on Points Of Interests (POIs) of user’s emerge in many studies and enterprise applications. Most of the preceeding travel recommendation methodologies are capable of recommending a personalized travel by considering the POIs. The major objective of this paper is to design a new Multi-Ontology based Points of Interest (MO-POIs) travel recommendation system which considers the POIs of the user along with the individual semantic information. Initially stage, the personalized MO-POIs sequence is recommended, and the routes are ranked based on the individual depending on the package he uses. Both the user and route package similarities are evaluated using parallel fuzzy clustering algorithm in this research work. Map Reduce is used as a parallel computation model in this research which performs computation with the help of a couple of functions that are termed as map and reduce. The social users travel records are used to optimize the top ranked routes. Finally, the new MO-POIs recommendation system evaluation is finished with a billion of Flicker images, uploaded by thousands of users and also more than 24,000 travelogues that cover the estimated 850 and above travel POIs in nine renowned cities, and illustrates its efficiency. The results are measured using the performance metrics like average precision and weighted average precision. The evaluation concludes that the proposed algorithm is capable of performing well in recent works that uses travel logs.

Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

In this proposed method, with the aim of resolving the frequent route mining issue, by means of utilizing the Rapid Association Rule Mining (RARM) for frequent route mining, recurrently utilized routes as well as smaller distance routes are mined. As a result, support speedy decision of the route identify more willingly than the standard route optimization. Primarily, Multi‐Ontology based Points of Interest (MO‐POIs) model is presented that takes the POIs of user design in combination with the semantic info of the individual users. Furthermore, it as well takes another two steps that are along these lines: (1) routes ranking as per the similarity amid user package as well as routes packages are carried out by means of utilizing Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) as well as (2) route optimizing by Parallel Ant Colony Optimization (PACO) technique in keeping with identical social users' records. The graph model is denoted as the amount of ants in the population of the identical user records. Assess the RARM‐PSEFC recommendation system on a set of Flickr images uploaded by users as well as travel POIs in numerous cities and show its efficiency.

Parallel Semi-supervised enhanced fuzzy Co-Clustering (PSEFC) and Rapid Association Rule Mining (RARM) based frequent route mining algorithm for travel sequence recommendation on big social media

Nanobiotechnology connects the scientific openings between chemistry, physics and biology on the nanoscale. These guides to a lot of modern techniques help and create good result in medical-phtherapeutic appliances. Now a day nanoparticles good aspirants for drug discovery and new therapeutic applications. It consists of the nanotechnology and biotechnology. Nanobiotechnology will help the combination of therapeutics with diagnostics and make easy to improve the specific medicine suitability for an individual and prevent the immune system from side effect drugs delivery. This article explores the nano biotechnology y for detecting specific disease in medical field.

N. Suresh Kumar

Papers

Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

Parallel Semi-supervised enhanced fuzzy Co-Clustering (PSEFC) and Rapid Association Rule Mining (RARM) based frequent route mining algorithm for travel sequence recommendation on big social media

Exploring Nano Biotechnology for detecting specific disease in Medical diagnosis and Therapeutic