When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

A Deviant Load Shedding System for Data Stream Mining

AbstractLoad shedding is imperative for data stream processing systems in numerous functions as data streams are susceptible to sudden spikes in volume. The proposed system is an attempt to seek and resolve four major problems associated with data stream, which include load shedding and anti-shedding time, number of transactions pruned and selecting predicate; using efficient mining system. The frequent pattern discovered in data stream used in the model exploits the synergy between scheduling and load shedding. This paper also proposes various load shedding strategies which reduce and lighten the workload of the system ensuring an acceptable level of mining accuracy using various parameters like transaction, priority and attributes of data mining. A majority chunk of workload in mining algorithm lies in the innumerable item sets, which are counted and enumerated. The approach is based on the frequent pattern matching principle of stream mining which involves reducing the workload to maintain smaller item sets

Adaptive Mining Techniques for Data Streams Using Algorithm Output Granularity Mohamed

Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing /mining data streams is the high data rate of the stream. In this paper, we propose a novel approach to cope with the high data rate of incoming data streams. We termed our approach "algorithm output granularity". It is a resource-aware approach that is adaptable to available memory, time constraints, and data stream rate. The approach is generic and applicable to clustering, classification and counting frequent items mining techniques. We have developed a data stream clustering algorithm based on the algorithm output granularity approach. We present this algorithm and discuss its implementation and empirical evaluation. The experiments show acceptable accuracy accompanied with run-time efficiency. They show that the proposed algorithm outperforms the K-means in terms of running time while preserving the accuracy that our algorithm can achieve

Effective Use Methods for Continuous Sensor Data Streams in Manufacturing Quality Control

This work outlines an approach for managing sensor data streams of continuous numerical data in product manufacturing settings, emphasizing statistical process control, low computational and memory overhead, and saving information necessary to reduce the impact of nonconformance to quality specifications. While there is extensive literature, knowledge, and documentation about standard data sources and databases, the high volume and velocity of sensor data streams often makes traditional analysis unfeasible. To that end, an overview of data stream fundamentals is essential. An analysis of commonly used stream preprocessing and load shedding methods follows, succeeded by a discussion of aggregation procedures. Stream storage and querying systems are the next topics. Further, existing machine learning techniques for data streams are presented, with a focus on regression. Finally, the work describes a novel methodology for managing sensor data streams in which data stream management systems save and record aggregate data from small time intervals, and individual measurements from the stream that are nonconforming. The aggregates shall be continually entered into control charts and regressed on. To conserve memory, old data shall be periodically reaggregated at higher levels to reduce memory consumption

A new data stream mining algorithm for interestingness-rich association rules

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. proficiency of the mined rules. However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm. Also, those few algorithms consider only the overall utility of the association rules and not the consistency of the rules throughout a defined number of periods. To solve this issue, in this paper, an enhanced association rule mining algorithm is proposed. The algorithm introduces new weightage validation in the conventional association rule mining algorithms to validate the utility and its consistency in the mined association rules. The utility is validated by the integrated calculation of the cost/price efficiency of the itemsets and its frequency. The consistency validation is performed at every defined number of windows using the probability distribution function, assuming that the weights are normally distributed. Hence, validated and the obtained rules are frequent and utility efficient and their interestingness are distributed throughout the entire time period. The algorithm is implemented and the resultant rules are compared against the rules that can be obtained from conventional mining algorithms

When things matter: A survey on data-centric Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed