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Managing and Mining Sensor Data
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“For all three tasks data has been addressed both in an online and offline manner, with more alarm related tasks being naturally used in the context of online and continuous monitoring [1].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
In the citing paper
“According to [1,19], most healthcare systems are dealing with the following issues: (1) data acquisition using the adequate sensor set; (2) transmission of data from subject to clinician; (3) integration of data with other descriptive data; and (4) data storage.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
In the citing paper
“Preprocessing in the healthcare domain involves (1) filter unusual data to remove artifacts and (2) remove high frequency noise [1,13].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
In the citing paper
“The main challenges of the preprocessing phase in healthcare systems are addressed in [1] which includes data formatting, data normalization, and data synchronization.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
In the citing paper
“This means that in order to make sense of the data, appropriate data processing techniques are essential [1].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [1]
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“Techniques for cleaning sensor data do exist [33], but uncertainty of sensor data may still lead to wrong conclusions about the current context, which in turn potentially lead to incorrect behaviors of the system.”
BackgroundHeart Health Risk Assessment System: A Nonintrusive Proposal Using Ontologies and Expert Rules
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“Of course, it should be a subject of a separate course among other data-mining techniques [10].”
BackgroundMinimal weighted infrequent itemset mining-based outlier detection approach on uncertain data stream
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“However, real-life applications, such as wireless sensor network data, personal identification data or location-based services data, can be uncertain [2].”
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“An online variational inference method, which exponentially forgets old data, was proposed by Honkela and Valpola [24], but similarly to [22,23], this approach suffers from the problem of setting the exponential forgetting rate.”
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Cai, M., Luo, H., Meng, X., & Cui, Y. (2021) · Sensors · doi:10.3390/s21134516 · Cited by 17
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“On the one hand, the term data reduction in the context of data mining is usually applied [18] to projects where the goal is to aggregate, or amalgamate information contained in large datasets.”
BackgroundA scalable search engine for mass storage smart objects
Anciaux, N., Lallali, S., Popa, I. S., et al. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2777598.2777600 · Cited by 14
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Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines
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Presenting a hybrid model in social networks recommendation system architecture development
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“This scope has been considered for years as a common interest in industry and academia (Aggarwal 2013).”
BackgroundEvaluating Geo-Tagged Twitter Data to Analyze Tourist Flows in Styria, Austria
Scholz, J., & Jeznik, J. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9110681 · Cited by 15
Support Dementia: Using Wearable Assistive Technology and Analysing Real-Time Data
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“Therefore, the combination of event processing for dealing with volume of data and semantics to manage several streams and background ontologies can improve the recognition of important situations in a timely manner [13]”
BackgroundData Reliability and Quality in Body Area Networks for Diabetes Monitoring
Huzooree, G., Khedo, K. K., & Joonas, N. (2018) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-00865-9_4 · Cited by 13
IoT and Data Visualization to Enhance Hyperlocal Data in a Smart Campus Context
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Multisource and Multitemporal Data Fusion in Remote SensingPreprint
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“ng data and local-but-live location-based social media data can offer new opportunities for smart city and smart environment applications with the “ground reference” collection through social sensing [197] and crowd sensing [198], in particular for hazards and disaster identification or tracking [192], [199]–[201]. To…”
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Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops
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“An event takes place, which refers to its occurrence, while an event is recognized by the system, which refers to its detection [52].”
BackgroundAnimated chorem-based summaries of geographic data streams from sensors in real time
Bouattou, Z., Laurini, R., & Belbachir, H. (2017) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2017.03.002 · Cited by 12
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“Such monitoring requires stream mining methods for the continuous detection of abnormalities, or for performing continuous queries in the underlying data [21, 22, 26, 66, 79].”
BackgroundMethodSelf-citationContext aware model-based cleaning of data streams
Gill, S., Lee, B., & Neto, E. C. P. (2015) · Irish Signals and Systems Conference · doi:10.1109/issc.2015.7163762 · Cited by 11
“Among the outlier detection models used are k-means clustering, nearest neighbours and Tukeys boxplot rule [5].”
MethodSupporting secure keyword search in the personal cloud
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Anomaly Detection of Environmental Sensor Data using Recurrent Neural Network at the Edge Device
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“Due to large volumes of data, monitoring and reporting of analyzed results need to be processed at the edge side of generated data[1].”
BackgroundAn approach for assessing industrial IoT data sources to determine their data trustworthiness
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Functional exploratory data analysis for high‐resolution measurements of urban particulate matter
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“Below are some characteristics that reinforce the notion of social networks as being sensor systems [12,13]: Preprints (www.”
BackgroundSensor Analysis for the Internet of Things
Stanley, M., & Lee, J. (2018) · Synthesis lectures on algorithms and software in engineering · doi:10.2200/s00827ed1v01201802ase017 · Cited by 10
Human actions recognition in video scenes from multiple camera viewpoints
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A Study of Tourist Sequential Activity Pattern through Location Based Social Network (LBSN)
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“The latter is crucial in the analysis of the tourist activity patterns in a given region or place [26].”
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“For example, Aggarwal wrote a comprehensive book on managing and mining sensor data, which covers sensor data mining, processing and social sensing and the [59].”
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Design and Implementation of Marine Elevator Safety Monitoring System based on Machine Learning
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“Several techniques can be used to clean RFID data [3] or deal with losing RFID data [42].”
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A Recent Connected Vehicle - IoT Automotive Application Based on Communication Technology
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“As a result, systems for car cloud services can also be created as hybrid clouds, wherein some offerings, like user information inquiry, can be hosted on public cloud structures, and other lacking-important offerings, like traffic administration, need to be located on personal cloud structures [38].”
BackgroundModeling Agility in Internet of Things (IoT) Architecture
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“Introduction The study of multiagent system (MAS) has attracted the interests of researchers from various fields for its potential application prospects in distributed computing, sensor networks and multi-robots system [1–4].”
BackgroundData Reliability Challenge of Cyber-Physical Systems
Wang, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803801-7.00006-7 · Cited by 7
HMM-based predictive model for enhancing data quality in WSN
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“The objectives of WSN data prediction generally contain: (1) recovering received failure data; (2) forecasting prospective data [2,3].”
BackgroundCan we monitor the natural environment analyzing online social network posts? A literature review
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Labeling sensing data for mobility modeling
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Location-Aware Path Alignment in Process Mining
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“Data mining is one of the greatest tasks needed for the management of big sensing data stream [2].”
BackgroundMining Social Media to Measure Neighborhood Quality in the City of Atlanta
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Generic Semantization of Vehicle Data Streams
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“However, “major benefits of such sensor data can only be realized if and only if we have the infrastructure and mechanisms to synthesize, interpret, and apply this data intelligently via automated means” [7, p. 400].”
MethodA Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset
Gan, Q., Gong, L., Hu, D., et al. (2023) · Sensors · doi:10.3390/s23218678 · Cited by 5
Learned Compression of Nonlinear Time Series with Random Access
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DNA nanostructures based biosensor for the determination of aromatic compounds
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“Fitting of the linear regression data is most common and simple method for the calibration of sensing device (Aggarwal, 2013).”
MethodReservoir Pattern Sampling in Data Streams
Giacometti, A., & Soulet, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_21 · Cited by 5
“Many applications generate data streams, especially with the rise of network sensors [2] and the Internet of Things [8].”
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“[11] have discussed method to tackle the above disadvantage by permitting a user to look for a sensor that yield a given result o the inquiry time.”
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“Data mining in sensor networks is the process of extracting application-oriented models and patterns with acceptable accuracy from a continuous, rapid, and possibly non ended flow of data streams from sensor networks [3].”
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Optimization framework and clustering-based algorithm for energy-aware adaptive sensing
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Outlier Detection from Mixed Attribute Space Using Hybrid Model
Sunitha, L., Raju, M. B., Makka, S., et al. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27409-1_86 · Cited by 3
Anomaly detection in sensor data provided by combine harvesters
Gu, Y., Bernardi, A., Steckel, T., et al. (2016) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin.2016.7819364 · Cited by 3
“• Limited data collection: The collection of meaningful data is generally a huge challenge in the context of sensor processing [1].”
Background
Metadata
- Authors
- Aggarwal, C. C.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Charų C. Aggarwal
- ORCID
- Charų C. Aggarwal
- Venue
- Springer eBooks
- Publisher
- Springer US
- Published
- 2013
- OpenAlex
- W204230938
- Cited by
- 294
- References
- 0
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 9 · 2023: 13 · 2022: 12 · 2021: 21 · 2020: 25 · 2019: 28 · 2018: 42 · 2017: 30 · 2016: 24 · 2015: 34 · 2014: 34 · 2013: 8
- FWCI
- 21.52
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Keywords
- sensor data management, sensor data mining, sensor networks, sensor data acquisition, low-cost sensors
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