Browsing by Subject "Big data"
Now showing items 1-12 of 12
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Big and lean is beautiful: a conceptual framework for data-based learning in marketing management
(Emerald Publishing Limited, 2019-09-19)While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn't necessarily improve their decision-making. Managers would still be vulnerable ... -
A big data processing framework for self-healing internet of things applications
(IEEE, 2016)In this study, we introduce a big data processing framework that provides self-healing capability in the Internet of Things domain. We discuss the high-level architecture of this framework and its prototype implementation. ... -
Centrality and scalability analysis on distributed graph of large-scale e-mail dataset for digital forensics
(IEEE, 2020-12-10)Today's digital forensics software tools mostly do not offer automatic analysis methods to reveal evidences among huge amounts of digital files within hard disk images. It is important that finding evidence in digital and ... -
Combining big data and lean startup methods for business model evolution
(Springer, 2017-12)The continued survival of firms depends on successful innovation. Yet, legacy firms are struggling to adapt their business models to successfully innovate in the face of greater competition from both local and global ... -
Digital oil refinery: utilizing real-time analytics and cloud computing over industrial sensor data
(2018-12-14)This thesis addresses big data challenges seen in large-scale, mission-critical industrial plants such as oil refineries. These plants are equipped with heavy machinery (boilers, engines, turbines, etc.) that are continuously ... -
DILAF: A framework for distributed analysis of large-scale system logs for anomaly detection
(Wiley, 2019-02)System logs constitute a rich source of information for detection and prediction of anomalies. However, they can include a huge volume of data, which is usually unstructured or semistructured. We introduce DILAF, a framework ... -
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ... -
Incremental analysis of large-scale system logs for anomaly detection
(IEEE, 2019)Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations ... -
Modernizing data and cloud practices: A review and model proposal for future studies
(IEEE, 2023)The interaction of society is transforming towards connectivity and digital experiences. This results in big data which cannot be utilized and managed by companies in an efficient way. Despite various applications of the ... -
Provenance aware run-time verification of things for self-healing Internet of Things applications
(Wiley, 2019-02-10)We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. ... -
Scalable analysis of large-scale system logs for anomaly detection
(2019-05-30)System logs provide information regarding the status of system components and various events that occur at runtime. This information can support fault detection, diagnosis and prediction activities. However, it is a ... -
The timing database: An open-access, live repository for interval timing studies
(Springer, 2024-01)Interval timing refers to the ability to perceive and remember intervals in the seconds to minutes range. Our contemporary understanding of interval timing is derived from relatively small-scale, isolated studies that ...
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