This is the Power point presentation for Semester 1 seminar which is the part of my 1st year M Tech course in Computer Science. It is completely based on the work done and the research paper by DOCEAR team. Thanks to them.
An adaptive clustering and classification algorithm for Twitter data streamin...TELKOMNIKA JOURNAL
On-going big data from social networks sites alike Twitter or Facebook has been an entrancing
hotspot for investigation by researchers in current decades as a result of various aspects including
up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy.
Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which
can cluster data within a lesser computational time, especially for data streaming is needed. The presented
adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome
the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is
viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally
improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data
streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is
classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization.
At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to
discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs
dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of
presented approach comparing with the existing methods in terms of precision, recall, F-score,
convergence, ROC curve and accuracy.
Finding Your Literature Match - A Recommender SystemEdwin Henneken
In this document I give a non-technical description of what a recommender system is and how it helps in the process of finding relevant information in times of information overload
An adaptive clustering and classification algorithm for Twitter data streamin...TELKOMNIKA JOURNAL
On-going big data from social networks sites alike Twitter or Facebook has been an entrancing
hotspot for investigation by researchers in current decades as a result of various aspects including
up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy.
Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which
can cluster data within a lesser computational time, especially for data streaming is needed. The presented
adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome
the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is
viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally
improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data
streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is
classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization.
At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to
discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs
dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of
presented approach comparing with the existing methods in terms of precision, recall, F-score,
convergence, ROC curve and accuracy.
Finding Your Literature Match - A Recommender SystemEdwin Henneken
In this document I give a non-technical description of what a recommender system is and how it helps in the process of finding relevant information in times of information overload
An overview of Plasma Antennas product portfolio of beamsteering, beamforming antennas as used in the cellular, mobile, wireless, Oil & Gas and government and defence industries.
Also included is an overview of our ground breaking Plasma Antenna device (PSiAD) which is a monolithic silicon antenna that incorporates plasma technology to provide a fast switched beam antenna thus giving a beamsteering capability for radios operating in the 1GHz to 120GHz range.
Both the antenna ranges can be classed as smart as they allow for high power, wide bandwidth antennas that can be used for wireless backhaul, small cell backhaul, small cell access in 4G LTE and LTE-A deployments.
Some of the designs provide smart beamforming capability at the common satellite frequencies thus giving a Land/Mobile terminal with steering that can track a satellite - L-band, X-band, KU-band and Ka-band are all supported.
Finally our higher mmWave frequency solutions are ideal for high capacity backhaul, interference reduction in data demanding 4G and 5G cellular networks and we are currently researching support for mmWave 5G antenna with full beamsteering and beamforming.
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Running head DEPRESSION PREDICTION DRAFT1DEPRESSION PREDICTI.docxhealdkathaleen
Running head: DEPRESSION PREDICTION DRAFT 1
DEPRESSION PREDICTION DRAFT 3
Data Science and Big Data Analytics
Option 1- Depression Prediction in Digital World
Introduction
This paper explores human behavior to detect their depression levels in the digital world by analyzing their behavioral patterns. The digital world has reduced the interaction time between human encounters and made it an easily available interaction via social media. More than 80% of the human emotions are shared in social media since the physical human interaction has fallen drastically that everyone prefers social media rather than healthy human interactions. Social media is serving as a double edge sword when human emotions are considered since it has the ability to destruct happiness and also the ability to lift a person from depression and related issues. This leads to focus and emphasize on ethical usage of social media since 90% of the user lot are taking it for granted maintaining too many profiles on different pseudo names. As the platform has grown bigger many types of research have been taking place for understanding the current psychological situations of the world population.
Literature Review
The emotional analysis has gone into a research-level topic many researches have incorporated linguistic processing and content interpretation to understand the behaviors of an individual. This paper studies the human behaviors and their emotions by considering the sources like customer reviews on internet articles, social media postings, stock fluctuations, product reviews, newspaper reactions, etc. This paper uses K-mean clustering and Neural networks to efficiently understand the human behaviors which give the true values for false rejections and true rejections. Back Propagation Neural Networks helps in gathering the related patterns that define a particular emotion and thereby the subject emotions get narrowed down and if the subject is a close study material then it would be easy to diagnose the subject with the required solution
Deep Learning Neural Network in depression analysis
The recent advances in Data Science and analysis techniques lead to many groundbreaking researches and depression prediction is one such research where the technology is giving enough insights in the medical field. Digital world is the home ground for many depression related issues since the major population is spending too much of time on the social media and digital gadgets that they prefer sharing their dark sides and emotions in the form of social media postings and writings in wide range of platforms like blogs and community groups.
Link mining
Link mining is one of the prominent technologies in the data mining where the data instances are linked through wide range of data models that can categorize the content into various groups based on the prime focus and subject of the sentences. Unstructured data is not considered in this aspect since the Link min ...
This chapter gives information about Social media analytics, Social network analysis, Text analytics, stopwords, tokenization, n-grams, Trend analytics, TF-IDF, Stemming and lemmatization
TEXT MINING-TAPPING HIDDEN KERNELS OF WISDOMITC Infotech
This paper discusses how automatic document classification, information retrieval, word frequency calculation, sentiment analysis, topic modelling and trend analysis can be utilized for root cause analysis, devising competitive strategies, enhancing customer experience and so on.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
An overview of Plasma Antennas product portfolio of beamsteering, beamforming antennas as used in the cellular, mobile, wireless, Oil & Gas and government and defence industries.
Also included is an overview of our ground breaking Plasma Antenna device (PSiAD) which is a monolithic silicon antenna that incorporates plasma technology to provide a fast switched beam antenna thus giving a beamsteering capability for radios operating in the 1GHz to 120GHz range.
Both the antenna ranges can be classed as smart as they allow for high power, wide bandwidth antennas that can be used for wireless backhaul, small cell backhaul, small cell access in 4G LTE and LTE-A deployments.
Some of the designs provide smart beamforming capability at the common satellite frequencies thus giving a Land/Mobile terminal with steering that can track a satellite - L-band, X-band, KU-band and Ka-band are all supported.
Finally our higher mmWave frequency solutions are ideal for high capacity backhaul, interference reduction in data demanding 4G and 5G cellular networks and we are currently researching support for mmWave 5G antenna with full beamsteering and beamforming.
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Running head DEPRESSION PREDICTION DRAFT1DEPRESSION PREDICTI.docxhealdkathaleen
Running head: DEPRESSION PREDICTION DRAFT 1
DEPRESSION PREDICTION DRAFT 3
Data Science and Big Data Analytics
Option 1- Depression Prediction in Digital World
Introduction
This paper explores human behavior to detect their depression levels in the digital world by analyzing their behavioral patterns. The digital world has reduced the interaction time between human encounters and made it an easily available interaction via social media. More than 80% of the human emotions are shared in social media since the physical human interaction has fallen drastically that everyone prefers social media rather than healthy human interactions. Social media is serving as a double edge sword when human emotions are considered since it has the ability to destruct happiness and also the ability to lift a person from depression and related issues. This leads to focus and emphasize on ethical usage of social media since 90% of the user lot are taking it for granted maintaining too many profiles on different pseudo names. As the platform has grown bigger many types of research have been taking place for understanding the current psychological situations of the world population.
Literature Review
The emotional analysis has gone into a research-level topic many researches have incorporated linguistic processing and content interpretation to understand the behaviors of an individual. This paper studies the human behaviors and their emotions by considering the sources like customer reviews on internet articles, social media postings, stock fluctuations, product reviews, newspaper reactions, etc. This paper uses K-mean clustering and Neural networks to efficiently understand the human behaviors which give the true values for false rejections and true rejections. Back Propagation Neural Networks helps in gathering the related patterns that define a particular emotion and thereby the subject emotions get narrowed down and if the subject is a close study material then it would be easy to diagnose the subject with the required solution
Deep Learning Neural Network in depression analysis
The recent advances in Data Science and analysis techniques lead to many groundbreaking researches and depression prediction is one such research where the technology is giving enough insights in the medical field. Digital world is the home ground for many depression related issues since the major population is spending too much of time on the social media and digital gadgets that they prefer sharing their dark sides and emotions in the form of social media postings and writings in wide range of platforms like blogs and community groups.
Link mining
Link mining is one of the prominent technologies in the data mining where the data instances are linked through wide range of data models that can categorize the content into various groups based on the prime focus and subject of the sentences. Unstructured data is not considered in this aspect since the Link min ...
This chapter gives information about Social media analytics, Social network analysis, Text analytics, stopwords, tokenization, n-grams, Trend analytics, TF-IDF, Stemming and lemmatization
TEXT MINING-TAPPING HIDDEN KERNELS OF WISDOMITC Infotech
This paper discusses how automatic document classification, information retrieval, word frequency calculation, sentiment analysis, topic modelling and trend analysis can be utilized for root cause analysis, devising competitive strategies, enhancing customer experience and so on.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
Graph embedding approach to analyze sentiments on cryptocurrencyIJECEIAES
This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
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journal of engineering, online Submission
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
Similar to My 1st semester seminar of M. Tech Part I (20)
Hybrid Approach to English-Konkani Machine TranslationSunayana Gawde
In this slideshow, I presented my research work in Machine Translation as my M.Tech Thesis. I developed English-Konkani Machine Translation system using various preprocessing and postprocessing steps so as to improve the quality of the translation.
In this presentation, I tried to do study on effect of Morphological Segmentation and De-segmentation on the effect and quality of Machine translation with respect to English-Konkani Translation.
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These slides are based on the research paper A MIND MAP QUERY IN INFORMATION RETRIEVAL by Rihab Ayed, Farah Harrathi, M. Mohsen Gammoudi and Mahran Farhat. Also i referred internet for additional material
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Pushing the limits of ePRTC: 100ns holdover for 100 days
My 1st semester seminar of M. Tech Part I
1. UTILIZING MIND-MAPS FOR INFORMATION
RETRIEVAL AND USER MODELLING
By Ms. Sunayana R. Gawde
M Tech in Computer Science
14109
2. ORIGINAL PAPER
On Utilizing Mind-Maps for Information Retrieval
and User Modelling:
By:
Joeran Beel
Stefan Langer
Marcel Genzmehr
Bela Gipp
3. CONCEPT
A mind map is a diagram used to visually organize
information. A mind map is often created around a
single concept and drawn as an image.
Major ideas are connected directly to the central
concept, and other ideas branch out from those.
As such they are often used for tasks including
brainstorming, project management and document
drafting.
5. TWO TYPES OF INFORMATION RETRIEVAL
APPLICATIONS, WHICH UTILIZED MIND-MAPS IN
PRACTICE.
Search Engine for Mind Maps
By MindMeister and XMind
User Modelling System-ads
By MindMeister and Mindomo
7. SEARCH ENGINES FOR MIND-MAPS
Search Engines for Mind-Maps
User Modelling
Document Indexing / Anchor Text Analysis
Document Relatedness
Document Summarization
Impact Analysis
Trend Analysis
Semantic Analysis
8. SEARCH ENGINES FOR MIND-MAPS:
Mind-maps contain information that probably is not
only relevant for the given authors of a mind-map,
but also for others.
Therefore a search engine for mind-maps might be
an interesting application.
9. USER MODELLING:
Analogous to analyzing users’ authored research
papers, emails, etc., user modelling systems could
analyze mind-maps to identify users’ information
needs and expertise. User models could be used,
for instance, for personalized advertisements, or by
recommender systems, or expert search systems
10. DOCUMENT INDEXING / ANCHOR TEXT
ANALYSIS:
Mind-maps could be seen as neighbouring
documents to those documents being linked in the
mind-maps, and anchor text analysis could be
applied to index the linked documents with the
terms occurring in the mind-maps. Such information
could be valuable, e.g., for classic search engines.
11. DOCUMENT RELATEDNESS:
When mind-maps contain links to web pages or
other documents, these links could be used to
determine relatedness of the linked web pages or
documents. For instance, with citation proximity
analysis, documents would be assumed to be
related that are linked in close proximity, e.g. in the
same sentence. Such calculations could be
relevant for search engines and recommender
systems
12. DOCUMENT SUMMARIZATION:
Mind-maps could be utilized to complement
document summarization. If a mind-map contains a
link to a web-page, the node’s text, and maybe the
text of parent nodes, could be interpreted as a
summary for the linked web page. Such summaries
could be displayed by search engines on their
result pages.
13. IMPACT ANALYSIS
Mind-maps could be utilized to analyze the impact
of the documents linked within the mind-map,
similar to PageRank or citation based similarity
metrics. This information could be used by search
engines to rank, e.g., web pages, or by institutions
to evaluate the impact of researchers and journals.
14. TREND ANALYSIS
Trend analysis is important for marketing and
customer relation- ship management, but also in
other disciplines . Such analyses could be done
based on mind-maps. For instance, analyzing mind-
maps that stand for drafts of academic papers
would allow estimating citation counts for the
referenced papers. It would also predict in which
field new papers can be expected.
15. SEMANTIC ANALYSIS
A mind-map is a tree and nodes are in hierarchical
order. As such, the nodes and their terms are in
direct relationship to each other. These
relationships could be used, for instance, by search
engines to identify synonyms, or by recommender
systems to recommend alternative search terms or
social tags.
17. 1. NUMBER OF MIND-MAP USERS AND
(PUBLIC) MIND-MAPS
18. 2. CONTENT OF MIND-MAPS
Analyzed the content of 19,379 mind-maps, created
by 11,179 MindMeister and Docear users.
On average, mind-maps contained a few dozens of
nodes, each with two to three words on average.
The number of links in mind-maps is low.
Almost two thirds of the mind-maps did not contain
any links to files.
21. PROTOTYPE
Click- through rate (CTR), i.e. the ratio of clicked
recommendations against the number of displayed
recommendations.
Primarily used by researchers.
Recommender system recommends research
papers
Each time, a user modified, i.e. edited or created, a
node, the terms of that node were send as search
query to Google Scholar.
23. REFERENCES
Beel, J., Langer, S., Genzmehr, M., Nürnberger, A.:
Introducing Docear’s Research Paper
Recommender System. Proceedings of the 13th
ACM/IEEE-CS Joint Conference on Digital Libraries
(JCDL’13). pp. 459–460. ACM (2013).
Beel, J., Gipp, B., Langer, S., Genzmehr, M.:
Docear: An Academic Literature Suite for
Searching, Organizing and Creating Academic
Literature. Proceedings of the 11th International
ACM/IEEE conference on Digital libraries. pp. 465–
466. ACM (2011).