ارائه یک معماری عامل گرا برای کاوش معنایی از داده‌های بزرگ مقیاس در محیط های توزیع شده

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مربی دانشگاه جامع امام حسین(ع)

2 دانشیار دانشگاه علم و صنعت ایران

3 استاد دانشگاه جامع امام حسین(ع)

چکیده

داده­های بزرگ مقیاس، متشکل از داده­های حجیم، توزیع شده، پراکنده، ناهمگون و ترکیبی از داده­های نامتجانس، بی ربط، گمراه کننده، واقعی و غیر واقعی است. بنابراین تجزیه و تحلیل، ایجاد ارزش و بهره­وری از داده­ها، همواره چالشی مهم و باز محسوب می شود. بنابراین هدف این پژوهش ارائه یک معماری ائتلافی جدید برای تولید اطلاعات با ارزش برای تصمیم‌­گیری از میان انبوه داده­ها است. معماری پیشنهادی که به اختصار ASMLDE  نامیده می­شود، با هدف توسعه و بهبود داده­کاوی، کاوش معنایی و تولید قواعد سودمند و با کیفیت از چهار لایه، هفت مؤلفه و شش عامل اصلی تشکیل می­شود. در معماری پیشنهادی برای جمع‌آوری و استانداردسازی پردازش‌های کیفی و تفسیرهای پیچیده‌تر، از مفهوم‌سازی با فرآیند v ’s4،  بینش از حجم و مقیاس داده‌ها در قالب مدل V’s3 و درنهایت بینش کیفی مبتنی بر ضخامت داده‌ها استفاده‌ شده است. این معماری با حمایت هستان­شناسی و عامل­کاوی، فضاهای بزرگ کاوش را کوچک‌تر و سرعت و کیفیت عملیات داده­کاوی را به دلیل به‌کارگیری سامانه­های چند عاملی افزایش می­دهد. خودکار­سازی عملیات کاوش، کاهش پیچیدگی داده­­ها و فرآیندهای کسب‌وکار نیز از مهم‌ترین دستاوردهای معماری پیشنهادی است. به‌منظور ارزیابی معماری پیشنهادی، مجموعه داده­ای بزرگ مقیاس از دامنه حوادث طبیعی و کلاس هستان‌شناسی زمین لرزه از پایگاه دانش DBpedia مورد استفاده قرار گرفته است. نتایج ارزیابی که حاصل از کاوش قواعد معنایی روی مجموعه داده­ای ذکر شده است، اثربخشی و  قابلیت­های معماری ASMLDE را در افزایش کیفیت قواعد معنایی کاوش شده متناسب با نیاز کاربر و کوچک‌تر کردن فضای بزرگ داده­کاوی نسبت به سایر چارچوب­ها و معماری­های مشابه نشان می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Providing an Agent-Based Architecture for Semantic Mining From Large-Scale Data in Distributed Environments

نویسندگان [English]

  • hussein saberi 1
  • M. R. Kangavari 2
  • M. R. Hasani Ahangar 3
1 Imam Hussein comprehensive university
2 iust
3 IHU
چکیده [English]

Large-scale data may consist of big, distributed, scattered, heterogeneous, irrelevant, misleading, real, and unrealistic data or any combination of them. Therefore, analyzing, creating value and data productivity is always an        important and open challenge. Therefore, the purpose of this study is to present a new coalition architecture for      generating valuable information for decision making among the masses of data. The proposed architecture,             abbreviated ASMLDE, aims to develop and improve data mining and semantic exploration, and to produce useful and high-quality rules consisting of four layers, seven components and six key elements. In the proposed architecture,   conceptualization with 4v's process, insight into the volume and scale of data in the form of 3v's model and finally qualitative insight based on data thickness, are used for conceptualization and standardization of qualitative processes and more complex interpretations. This architecture, supported by ontology and agent mining, reduces large search spaces and increases the speed and quality of data mining operations due to the use of multi-agent systems.             Automating exploration operations, reducing data complexity and business processes are also important achievements of the proposed architecture. To evaluate the proposed architecture, a large-scale dataset of natural disasters and earthquake ontology classes from the DBpedia knowledge base have been used. The evaluation results obtained by exploring the semantic rules of the mentioned dataset highlight the effectiveness and capabilities of the ASMLDE        architecture in enhancing the quality of the semantic rules explored to fit the user need and reducing the large data mining space over other similar frameworks and architectures.
 

کلیدواژه‌ها [English]

  • Large Scale Data
  • Semantic Mining
  • Ontology
  • Agent-Oriented Architecture  

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سابقه مقاله

  • تاریخ دریافت: 01 آبان 1397
  • تاریخ بازنگری: 21 دی 1397
  • تاریخ پذیرش: 14 اسفند 1397
  • تاریخ انتشار: 01 آبان 1399

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