A Hierarchical Graph Model for Disease Identification Based on Basic Symptoms
TL;DR: A graph model based system is proposed where the people can give simple symptoms to diagnose their diseases and a hierarchical graph named as “Hierarchy Graph for Disease Identification” (HGDI) is proposed for this purpose.
Abstract: Medical data analysis is an important area of research in computational science. Many methods are proposed to carry out the analysis. In a country like India medical facility is not available in many rural areas due to the lack of doctors or hospital facilities. Therefore the people don’t get the proper medication rather the fake doctors (quarks) gave them wrong medications. This is a big problem for the rural areas of many countries including India. However due to the rapid growth of internet and mobile phones, people in rural areas are accessing the internet regularly and habituated to the use of it. Therefore if an interactive medical system is given to them in the form of apps they can use it easily. In this research work a graph model based system is proposed where the people can give simple symptoms to diagnose their diseases. A hierarchical graph named as “Hierarchy Graph for Disease Identification” (HGDI) is proposed for this purpose. This could be implemented in apps /software for real life usage.
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TL;DR: The author develops a property graph data model to facilitate the process model of knowledge management and implements this model through the Neo4j graph database system.
Abstract: Emerging technologies let companies manage their knowledge assets with more innovative and effective methods. Due to the complex nature of knowledge management processes, it is cumbersome to design, develop, and implement a system based on relational databases. This article proposes a specific graph database application in streamlining major knowledge management processes. The author develops a property graph data model to facilitate the process model of knowledge management. In addition, this property graph data model is implemented through the Neo4j graph database system. This research provides some guidance for practitioners in seeking alternative approaches to traditional methods of knowledge management.
25 citations
"A Hierarchical Graph Model for Dise..." refers methods in this paper
...Graph database has been used for knowledge management using property graph data model [1]....
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TL;DR: This paper develops a marketing campaign recommender system that incorporates social factors, such as community relationships of the business customers, for further improving overall performances of the missing edge prediction and recommendation and shows that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks.
Abstract: Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers, and thus entails management of more complex business needs than consumer marketing. The buying processes of the business customers involve series of different marketing campaigns providing multifaceted information about the products or services. While most existing studies focus on individual consumers, little has been done to guide business customers due to the dynamic and complex nature of these business buying processes. To this end, in this paper, we focus on providing a unified view of social and temporal modeling for B2B marketing campaign recommendation. Along this line, we first exploit the temporal behavior patterns in the B2B buying processes and develop a marketing campaign recommender system. Specifically, we start with constructing a temporal graph as the knowledge representation of the buying process of each business customer. Temporal graph can effectively extract and integrate the campaign order preferences of individual business customers. It is also worth noting that our system is backward compatible since the participating frequency used in conventional static recommender systems is naturally embedded in our temporal graph. The campaign recommender is then built in a low-rank graph reconstruction framework based on probabilistic graphical models. Our framework can identify the common graph patterns and predict missing edges in the temporal graphs. In addition, since business customers very often have different decision makers from the same company, we also incorporate social factors, such as community relationships of the business customers, for further improving overall performances of the missing edge prediction and recommendation. Finally, we have performed extensive empirical studies on real-world B2B marketing data sets and the results show that the proposed method can effectively improve the quality of the campaign recommendations for challenging B2B marketing tasks.
21 citations
"A Hierarchical Graph Model for Dise..." refers background or methods in this paper
...Then a campaign recommender is constructed in a low-rank graph reconstruction framework based on probabilistic graphical models [5]....
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...In Business to Business (B2B) marketing a temporal graph [5] is constructed to represent the buying process....
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TL;DR: This paper focuses on integrating XML data based on multiple related XML schemas, to an equivalent data warehouse schemas based on relational online analytical processing (ROLAP) and a new data structure, Schema Graph has been proposed in the process.
Abstract: Data Warehouse is one of the most common ways for analyzing large data for decision based system. These data are often sourced from online transactional system. The transactional data are represented in different formats. XML is one of the worldwide standards to represent data in web based system. Numbers of organizations use XML for e-commerce and internet based applications. Integration of XML and data warehouse for the innovation of business logic and to enhance decision making has therefore emerged as a demanding area of research interest. This paper focuses on integrating XML data based on multiple related XML schemas, to an equivalent data warehouse schemas based on relational online analytical processing (ROLAP). This work bears a high relevance towards standardizing of the ETL phase (Extraction, Transformation, and Loading) of the OLAP projects. The novelty of the work is that more than one data warehouse schemas could be identified from a single related XML schema and each of them could be categorized as star schema or snowflake schema. Moreover if the individual schemas are found to be related according to the analysis, fact constellation could be identified. A new data structure, Schema Graph has been proposed in the process.
15 citations
"A Hierarchical Graph Model for Dise..." refers methods in this paper
...In semistructured database applications such as XML, graph has been used for ROLAP (Relational Online Analytical Processing) warehouse schemas [7]....
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...A new graph model Schema Graph [7] has proposed to convert XML schemas to data warehouse schemas....
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01 Jan 2016
TL;DR: Here, use of property graph model is demonstrated for solution of state-of-affairs hybrid recommendation system, complexity of beneath integrated data structure and required course of actions.
Abstract: Graph database is revealed as alternative of traditional relation database for the reason that graph is flexible and self-explaining structure, which can cope with any kind of complex structure. Recently, graph database is extensively used to represent specially multi linked data of web, RDF data, social network, chemical structure, gene, network structure, publication links, and many more. This research illustrates potential use of graph database for recommendation system along with its convenience to develop hybrid recommendation system. Here, use of property graph model is demonstrated for solution of state-of-affairs hybrid recommendation system, complexity of beneath integrated data structure and required course of actions. This investigation anticipates an appropriate use of graph database to integrate various recommendation algorithms like content based recommendation; utility based recommendation as well as knowledge based recommendation. The main intention behind development of hybrid model is that helps in solving challenges of real world like cold start and many more. This research provides complete guidelines to anyone who wants to implement graph database for recommendation system along with various recommendation algorithms.
13 citations
"A Hierarchical Graph Model for Dise..." refers methods in this paper
...A hybrid model has been proposed in [2] to as an alternative to relational model to integrate utility based recommendation, content based recommendation and knowledge based recommendation....
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TL;DR: A new image modeling method is introduced by getting benefit from both sparsity and multiscale characteristics of transform-domain modeling, along with the geometrical representation of the graph-based models and improves directional selectivity property of the bandlets.
Abstract: In this paper, we introduce a new image modeling method by getting benefit from both sparsity and multiscale characteristics of transform-domain modeling, along with the geometrical representation of the graph-based models. The proposed method is named bandlet on an oriented graph (BOG) and improves directional selectivity property of the bandlets. The conventional wavelet in the bandlet design is substituted with a new non-orthogonal wavelet. The replaced wavelet is defined on a graph. In order to adjust the orientation of the wavelet atoms with the corresponding edges in the image pixels, a directed graph is constructed. The resultant wavelets in discrete scales can be considered as a frame and are created to build a tight frame. To show the effectiveness of this new atomic representation, we demonstrated the performance of the new model in noise alleviation of the optical coherence tomography (OCT) images (from the retina) and microscopic images. Denoising results on OCT are reported on 72 slices, selected arbitrarily out of OCT dataset from Topcon device. The combined method provided an enhancement of contrast to noise ratio (CNR) (from 27.82 to 30.11), and improvement of the equivalent number of looks (ENL) (from 2183.26 to 2217.37) over the state-of-the-art in OCT noise reduction. In the denoising of microscopic images, PSNR improvement (from 26.33 to 35.24) over the original image is shown along with the improvement in next steps of feature extraction.
9 citations
"A Hierarchical Graph Model for Dise..." refers background in this paper
...Bandlet on an oriented graph (BOG) [11] has been proposed to improve the directional selectivity property of the bandlets....
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