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نتیجه جستجو - Data filtering

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 German solar power generation data mining and prediction with transparent open box learning network integrating weather, environmental and market variables
استخراج و پیش بینی داده های تولید انرژی خورشیدی آلمان با شبکه یادگیری جعبه باز شفاف متغیرهای آب و هوا ، محیط زیست و بازار-2019
A compiled dataset of hourly-averaged solar power generation (MW) for Germany in 2016 integrates eight influencing weather, environmental and market price variables (8784 data records including 5012 non-zero generation periods). It provides valuable insight to solar power variations over the course of one year. The dataset is evaluated with the transparent open box (TOB) learning network for data mining and prediction purposes. This algorithm provides accurate and repeatable MW predictions and enables detailed evaluation of the key influencing variables on each hourly data record. TOB Stage 1 applies a data matching routine driven by the squared errors between the independent variables. TOB Stage 2 applies a customized memetic firefly optimizer to minimize the root mean squared error (RMSE) for MW predictions over subsets and/or the full dataset. TOB achieves high prediction accuracy (average of five cases RMSE=1044.4 MW; R2=0.975) using tuning subsets of only ∼303 data records (∼6% of the full dataset). The dataset displays some significant MW prediction outliers that are readily identified and explained individually by the TOB algorithm’s data mining capabilities. A slightly filtered dataset (4918 data records excluding 94 outlier data records) improves MW prediction accuracy (average of five cases RMSE=936.1 MW; R2=0.980). Whereas the prediction outliers are readily segregated as a separate subset for more detailed evaluation. The TOB algorithm’s combined machinelearning and data mining capabilities provide valuable insight to the dataset and the influences of its independent variables. The algorithm couples high-prediction accuracy and detailed evaluation of long-term and short-term time series data and for spatial scales varying from country level to individual solar farms.
Keywords: German country-wide solar power generation | Machine learning transparency | Integrating environmental and market | variables | Data mining outlier analysis | Data filtering
مقاله انگلیسی
2 A secure kNN query processing algorithm using homomorphic encryption on outsourced database
یک الگوریتم پردازش پرس و جو kNN امن با استفاده از رمزگذاری همگن در پایگاه داده برون سپاری شده-2017
With the adoption of cloud computing, database outsourcing has emerged as a new platform. Due to the serious privacy concerns associated with cloud computing, databases must be encrypted before being outsourced to the cloud. Therefore, various k-nearest neighbor (kNN) query processing techniques have been proposed for encrypted databases. However, existing schemes are either insecure or inefficient. In this paper, we propose a new secure kNN query processing algorithm. Our algorithm guarantees the confidentiality of both encrypted data and users’ query records. To achieve a high level of query processing efficiency, we also devise an encrypted index search scheme that performs data filtering without revealing data access patterns. A performance analysis shows that the proposed scheme outperforms the existing scheme in terms of query processing costs while preserving data privacy.
Keywords: Database outsourcing | Database encryption | Encrypted index structure | Data Privacy | KNN query processing
مقاله انگلیسی
3 A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks
اکتشافی شناختی اجتماعی برای انتشار اطلاعات سازگار در شبکه های اپورتونیستی سیار-2017
It is commonly agreed that data (and data-centric services) will be one of the cornerstones of Future Internet systems. In this context, mobile Opportunistic Networks (OppNets) are one of the key paradigms to efficiently support, in a self-organising and decentralised manner, the growth of data generated by localized interactions between users mobile devices, and between them and nearby smart devices such as IoT nodes. In OppNets scenarios, the spontaneous collaboration among mobile devices is exploited to disseminate data toward interested users. However, the limited resources and knowledge available at each node, and the vast amount of data available in the network, make it difficult to devise efficient schemes to accomplish this task. Recent solutions propose to equip each device with data filtering methods derived from human information processing schemes, known as Cognitive Heuristics. They are very effective methods used by human brains to quickly drop useless information and keep only the most relevant information. Althought cognitive-based OppNet solutions proved to be efficient (with limited overheads), they can become less effective when facing dynamic scenarios or situations where nodes cannot fully collaborate with each other, as we show in this paper. One of the reasons is that the solutions proposed so far do not take take into account the social structure of the environment where the nodes are moving in. In order to be more effective, the selection of information performed by each node should take into consideration not only the relevance of content for the local device, but also for other devices will encounter in the future due to mobility. To this end, in this paper we propose a social-based data dissemination scheme, based on a cognitive heuristic, known as the Social Circle Heuristic. This heuristic is an evaluation method that exploits the structure of the social environment to make inferences about the relevance of discovered information. We show how the Social Circle Heuristic, coupled with a cognitive-based community detection scheme, can be exploited to design an effective data dissemination algorithm for OppNets. We provide a detailed analysis of the performance of the proposed solution via simulation.
Keywords: Opportunistic Networks | Cognitive Heuristics | Data Dissemination | Social | Self-Organising
مقاله انگلیسی
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