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ارائه یک معماری عامل گرا برای کاوش معنایی از دادههای بزرگ مقیاس در محیط های توزیع شده | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 7، دوره 8، شماره 3 - شماره پیاپی 31، آبان 1399، صفحه 83-99 اصل مقاله (1.66 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| حسین صابری* 1؛ محمدرضا کنگاوری2؛ محمد رضا حسنی آهنگر3 | ||
| 1مربی دانشگاه جامع امام حسین(ع) | ||
| 2دانشیار دانشگاه علم و صنعت ایران | ||
| 3استاد دانشگاه جامع امام حسین(ع) | ||
| تاریخ دریافت: 01 آبان 1397، تاریخ بازنگری: 21 دی 1397، تاریخ پذیرش: 14 اسفند 1397 | ||
| 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 | ||
| داده های بزرگ مقیاس؛ کاوش معنایی؛ هستانشناسی؛ معماری عامل‌گرا | ||
| عنوان مقاله [English] | ||
| Providing an Agent-Based Architecture for Semantic Mining From Large-Scale Data in Distributed Environments | ||
| نویسندگان [English] | ||
| hussein saberi1؛ M. R. Kangavari2؛ M. R. Hasani Ahangar3 | ||
| 1Imam Hussein comprehensive university | ||
| 2iust | ||
| 3IHU | ||
| 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 | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 610 تعداد دریافت فایل اصل مقاله: 340 |
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