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Task Exploration from E-mail Messages for Corporate Collaborative Processes and Development of a Real-Time Task Management System

Yıl 2017, Cilt: 10 Sayı: 4, 381 - 388, 30.10.2017
https://doi.org/10.17671/gazibtd.281713

Öz

Email
systems are one of the most basic tools used for corporate communication and
collaboration. The information flow is carried out via these systems for almost
all corporate tasks related to the basic workflows, including planning,
resource or project management. For this reason, email systems have become
service stores containing valuable information for companies, and very
important central areas where the tasks are managed. It is very particularly
important for customer-focused organizations to manage workflows via e-mail
services. Although it is possible to create work lists on customer level via
enterprise systems over central software, these requestes are mostly
transferred to employees' e-mail accounts. This case may cause such results as
the employee's not planning the tasks in his email address, forgetting, losing,
not defining its level of importance. In this study, a method is proposed that
labels the requests to be managed coming from the corporate e-mail account by
using text mining and classification techniques and provides input for the
to-do list application developed. The proposed method and the developed
application offer real-time collaborative working environment with the
extensible messaging and its integration with the status protocol. Thus, the
conversion process of corporate e-mails to work lists has been adapted to
collaborative work though the real-time status management approach over the
corporate instant messaging systems.

Kaynakça

  • G. Tang, J. Pei, W. S. Luk, “Email Mining: Tasks, Common Techniques, and Tools”, Knowledge and Information Systems, 41(1), 1-31, 2014.
  • M. Suit, H. Wortmann, “Discovery and analysis of e-mail-driven business processes”, Information Systems, 37(2), 142-168, 2012.
  • K. Coussement, D. V. Poel, Improving customer complaint management by automatic email classification using linguistic style features as predictors, Decision Support Systems, 44(4), 870-882, 2008.
  • L. Dey, S. Bharadwaja, G. Meera, G. Shroff, Email Analytics for Activity Management and Insight Discovery, IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), 557-564, 2013.
  • S. S. Weng, C. K. Liu, “Using text classification and multiple concepts to answer e-mails”, Expert Systems with applications, 26(4), 529-543, 2004.
  • S. Appavu, R. Rajaram, M. Muthupandian, G. Athiappan, K. S. Kashmeera, “Data mining based intelligent analysis of threatening e-mail”, Knowledge-Based Systems, 22(5), 392-393, 2009.
  • B. Yu, D. Zhu, “Combining neural networks and semantic feature space for email classification”, Knowledge-Based Systems, 22(5), 376-381, 2009.
  • I. Alsmadi, I. Alhami, “Clustering and classification of email contents”, Journal of King Saud University - Computer and Information Sciences, 27(1), 46-57, 2015.
  • M. G. Armentano, A. A. Amandi, “Enhancing the experience of users regarding the email classification task using labels”, Knowledge-Based Systems, 71, 227-237, 2014.
  • M. F. Wan, M. F. Tsai, S. L. Jheng, C. H. Tang, “Social feature-based enterprise email classification without examining email contents”, Journal of Network and Computer Applications, 35(2), 770-777, 2012.
  • D. C. Soares, F. M. Santoro, F. A. Baiao, “Discovering collaborative knowledge-intensive processes through e-mail mining”, Journal of Network and Computer Applications, 36(6), 1451-1465, 2013.
  • J. R. Méndez, M. Reboiro-Jato, F. Díaz, E. Díaz, F. Fdez-Riverola, “Grindstone4Spam: An optimization toolkit for boosting e-mail classification”, The Journal of Systems and Software, 85(12), 2909-2920, 2012.
  • I. Koprinska, J. Poon, J. Clark, J. Chan, “Learning to classify e-mail”, Information Sciences, 177(10), 2167-2187, 2007.
  • P. Pankaj, M. Hyde, J. A. Rodger, “P2P Business Applications: Future and Directions”, Communications and Network, 4, 248-260, 2012.
  • S. V. Ragavana, I. K. Kusnantoa, V. Ganapathyb, “Service Oriented Framework for Industrial Automation Systems”, Procedia Engineering, 41, 716-723, 2012.
  • L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, Inc., 1994.
  • O. Kaynar, F. Demirkoparan, “Forecasting Industrial Production Index with Soft Computing Techniques”, Economic Computation and Economic Cybernetics Studies and Research, 46(3), 113-138, 2012.
  • J. Clark, I. Koprinska, J. Poon, A Neural Network Based Approach to Automated E-mail Classification, International Conference on Web Intelligence (WI’03), 702-705, 2003.
  • Internet: Openfire XMPP Server, a real time collaboration community, http://www.igniterealtime.org, 08.01.2016.
  • C. Cortes, V. Vapnik, “Support-Vector Networks”, Machine Learning, 20(3), 273-297, 1995.
  • E. H. S. Han, G. Karypis, “Centroid-Based Document Classification: Analysis and Experimental Results”, European conference on principles of data mining and knowledge discovery, 424-431, Springer Berlin Heidelberg, Eylül 2000.
  • Internet: A. A. Akin, M. D. Akin, NLP library, NZemberek 0.1.0, http://www.nuget.org/packages/NZemberek, 11.02.2016.

Kurumsal Kolektif Süreçler için E-Posta İletilerinden Görev Keşfi ve Gerçek Zamanlı Görev Yönetim Sisteminin Geliştirilmesi

Yıl 2017, Cilt: 10 Sayı: 4, 381 - 388, 30.10.2017
https://doi.org/10.17671/gazibtd.281713

Öz

E-Posta
sistemleri, kurumsal iletişim ve işbirliği için kullanılan en temel araçların
başında gelmektedir. Başta planlama, kaynak veya proje yönetimi olmak üzere
temel iş akışları ile ilgili neredeyse tüm kurumsal görevler için bilgi akışı
bu sistemler ile gerçekleştirilmektedir. Bu nedenle e-posta sistemleri,
şirketler için değerli bilgiler içeren hizmet depoları ve işlerin yönetildiği
çok önemli merkezi alanlar haline gelmiştir. Özellikle müşteri odaklı
kuruluşlar için e-posta servisleri üzerinden iş akışlarının yönetilebilir
olması çok önemlidir. Kurumsal sistemler ile müşteri düzeyindeki iş
listelerinin merkezi yazılımlar üzerinden oluşturulma imkanı olsa da bu
talepler çoğunlukla çalışanların e-posta hesaplarına iletilmektedir. Bu durum
çalışanın e-posta adresindeki işleri planlamaması, unutması, kaybolması, önem
düzeyini doğru belirleyememesi gibi sonuçlara yol açabilmektedir. Yapılan bu
çalışma ile personelin kurumsal e-posta hesaplarına gelen mesajlardan,
yönetilmesi gereken talepleri, metin madenciliği ve sınıflama teknikleri
kullanılarak etiketleyen ve geliştirilen yapılacaklar listesi (todo list)
uygulamasına girdi sağlayacak bir yöntem önerilmektedir. Önerilen yöntem ve
geliştirilen uygulama, genişletilebilir mesajlaşma ve durum protokolü üzerine
entegrasyonu ile gerçek zamanlı işbirlikçi çalışma olanağı sunmaktadır. Böylece
kurumsal e-postaların iş listesine dönüştürülme süreci, kurumsal anında
mesajlaşma sistemleri üzerinden gerçek zamanlı durum yönetim yaklaşımı ile
işbirlikçi çalışmaya uygun hale getirilmiştir.

Kaynakça

  • G. Tang, J. Pei, W. S. Luk, “Email Mining: Tasks, Common Techniques, and Tools”, Knowledge and Information Systems, 41(1), 1-31, 2014.
  • M. Suit, H. Wortmann, “Discovery and analysis of e-mail-driven business processes”, Information Systems, 37(2), 142-168, 2012.
  • K. Coussement, D. V. Poel, Improving customer complaint management by automatic email classification using linguistic style features as predictors, Decision Support Systems, 44(4), 870-882, 2008.
  • L. Dey, S. Bharadwaja, G. Meera, G. Shroff, Email Analytics for Activity Management and Insight Discovery, IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), 557-564, 2013.
  • S. S. Weng, C. K. Liu, “Using text classification and multiple concepts to answer e-mails”, Expert Systems with applications, 26(4), 529-543, 2004.
  • S. Appavu, R. Rajaram, M. Muthupandian, G. Athiappan, K. S. Kashmeera, “Data mining based intelligent analysis of threatening e-mail”, Knowledge-Based Systems, 22(5), 392-393, 2009.
  • B. Yu, D. Zhu, “Combining neural networks and semantic feature space for email classification”, Knowledge-Based Systems, 22(5), 376-381, 2009.
  • I. Alsmadi, I. Alhami, “Clustering and classification of email contents”, Journal of King Saud University - Computer and Information Sciences, 27(1), 46-57, 2015.
  • M. G. Armentano, A. A. Amandi, “Enhancing the experience of users regarding the email classification task using labels”, Knowledge-Based Systems, 71, 227-237, 2014.
  • M. F. Wan, M. F. Tsai, S. L. Jheng, C. H. Tang, “Social feature-based enterprise email classification without examining email contents”, Journal of Network and Computer Applications, 35(2), 770-777, 2012.
  • D. C. Soares, F. M. Santoro, F. A. Baiao, “Discovering collaborative knowledge-intensive processes through e-mail mining”, Journal of Network and Computer Applications, 36(6), 1451-1465, 2013.
  • J. R. Méndez, M. Reboiro-Jato, F. Díaz, E. Díaz, F. Fdez-Riverola, “Grindstone4Spam: An optimization toolkit for boosting e-mail classification”, The Journal of Systems and Software, 85(12), 2909-2920, 2012.
  • I. Koprinska, J. Poon, J. Clark, J. Chan, “Learning to classify e-mail”, Information Sciences, 177(10), 2167-2187, 2007.
  • P. Pankaj, M. Hyde, J. A. Rodger, “P2P Business Applications: Future and Directions”, Communications and Network, 4, 248-260, 2012.
  • S. V. Ragavana, I. K. Kusnantoa, V. Ganapathyb, “Service Oriented Framework for Industrial Automation Systems”, Procedia Engineering, 41, 716-723, 2012.
  • L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, Inc., 1994.
  • O. Kaynar, F. Demirkoparan, “Forecasting Industrial Production Index with Soft Computing Techniques”, Economic Computation and Economic Cybernetics Studies and Research, 46(3), 113-138, 2012.
  • J. Clark, I. Koprinska, J. Poon, A Neural Network Based Approach to Automated E-mail Classification, International Conference on Web Intelligence (WI’03), 702-705, 2003.
  • Internet: Openfire XMPP Server, a real time collaboration community, http://www.igniterealtime.org, 08.01.2016.
  • C. Cortes, V. Vapnik, “Support-Vector Networks”, Machine Learning, 20(3), 273-297, 1995.
  • E. H. S. Han, G. Karypis, “Centroid-Based Document Classification: Analysis and Experimental Results”, European conference on principles of data mining and knowledge discovery, 424-431, Springer Berlin Heidelberg, Eylül 2000.
  • Internet: A. A. Akin, M. D. Akin, NLP library, NZemberek 0.1.0, http://www.nuget.org/packages/NZemberek, 11.02.2016.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

Halil Arslan

Oğuz Kaynar

Ahmet Gürkan Yüksek

Yayımlanma Tarihi 30 Ekim 2017
Gönderilme Tarihi 27 Ocak 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 10 Sayı: 4

Kaynak Göster

APA Arslan, H., Kaynar, O., & Yüksek, A. G. (2017). Kurumsal Kolektif Süreçler için E-Posta İletilerinden Görev Keşfi ve Gerçek Zamanlı Görev Yönetim Sisteminin Geliştirilmesi. Bilişim Teknolojileri Dergisi, 10(4), 381-388. https://doi.org/10.17671/gazibtd.281713