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Managing and Mining Sensor Data
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In the citing paper
“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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Items 201–289 of 289
A study on Improvement of Resource Efficiency for IoT-based Pipe Leak Detection
Oh, S. W., Bae, J., Yoon, D.-B., et al. (2018) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2018.8539385 · Cited by 3
“As a series of acoustic signals are continuously collected, a proper system structure to handle such streaming data is required [13], [14], taking into account the overall system’s resource efficiency and low latency.”
BackgroundComplex Event Processing Based Analysis of Cassini–Huygens Interplanetary Dataset
ALDabbas, A., & Gál, Z. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-38501-9_5 · Cited by 3
Azar, E., & Menassa, C. C. (2014) · Computing in Civil and Building Engineering (2014) · doi:10.1061/9780784413616.142 · Cited by 3
“, a person enters or leaves the space) (Aggarwal 2013).”
BackgroundMethodInfluentialMining Sensor Data in Larger Physical Systems
O'Leary, P. L., Harker, M., Ritt, R., et al. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.10.093 · Cited by 3
FRDF: Framework for Reliable Data Fusion to Leverage Communication Performance in Sensor Network
Jayasri, B. S., & Rao, G. R. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-57141-6_2 · Cited by 3
“In order to solve this problem, a cluster head perform filtering of the redundant data using statistical computation, to forward the unique data to the base station [2].”
MethodWhich way forward: agricultural waste management and the fourth industrial revolution appraisal
Onu, P., & Mbohwa, C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85402-3.00010-3 · Cited by 4
Efficient forwarding strategy in HDRP protocol based Internet of Things
Tekouabou, S. C. K., Alaoui, E. A. A., & Gallais, A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.02.003 · Cited by 3
AgriFuture: A New Theory of Change Approach to Building Climate-Resilient Agriculture
Mousannif, H., & Zahir, J. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-11878-5_10 · Cited by 3
eVM: An Event Virtual Machine Framework
Mansour, E., Chbeir, R., & Arnould, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-662-58415-6_5 · Cited by 2
“In the literature, many works [2, 3] define events as a happening that takes place at a particular time and location.”
BackgroundIDTable: Self-Describing Relational Data for Resource-Constraint Smart Objects
Luo, W. (2018) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/padsw.2018.8644623 · Cited by 2
Khalid, M., Yusuf, A. O., Akanmu, A. A., et al. (2024) · International Journal of Construction Education and Research · doi:10.1080/15578771.2024.2404969 · Cited by 2
Survey on Sina Weibo Research Based on Big Data Mining
Wang, R. (2015) · doi:10.11648/j.ijdsa.20150101.11 · Cited by 2
“There are many applications of Big Data, for example the following [19]: Business : costumer personalization, churn detection.”
MethodAn Improved HMM Model for Sensing Data Predicting in WSN
Zhang, Z., Deng, B., Chen, S., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39937-9_3 · Cited by 2
Reuse of data from smart medical devices for quality control and evidence-based medicine
Sliwa, J. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803663-1.00009-7 · Cited by 2
Interfacing Physical and Cyber Worlds: A Big Data Perspective
Baloch, Z., Shaikh, F. K., & Unar, M. A. (2016) · Springer eBooks · doi:10.1007/978-3-319-31861-5_6 · Cited by 2
Social sensing trends and applications
Dong, W., Abdelzaher, T., & Kaplan, L. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800867-6.00002-9 · Cited by 2
Investigating the Feasibility of Vehicle Telemetry Data as a Means of Predicting Driver Workload
Taylor, P. M., Griffiths, N., Bhalerao, A. H., Zhou, X., Gelencser, A., & Popham, T. (2017) · International Journal of Mobile Human Computer Interaction · doi:10.4018/ijmhci.2017070104 · Cited by 2
“In vehicle telemetry mining, it is advantageous to include trend information about signals (Antunes & Oliveira, 2001; Tango & Botta, 2009; Wollmer et al., 2011; Aggarwal, 2013).”
BackgroundMethodModeling microstrip antennas for UHF RFID tags
Borisov, D. N., & Zuev, S. A. (2017) · doi:10.1109/icatt.2017.7972637 · Cited by 2
“0, based on the management multisession tags can increase the reading speed of 1500 tags per second, while effectively use multiple readers in close overlapping regions [5].”
BackgroundA Comparative Study of ANN Tuning Methods for Multiclass Daily Activity and Fall Recognition
Aktay, T., & Efe, M. Ö. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-37548-5_3 · Cited by 2
“This has led to enormous amounts of real-time data, which allows for a variety of analytical insights [2].”
BackgroundConsidering Currency in Decision Trees in the Context of Big Data
Hristova, D. (2014) · University of Regensburg Publication Server (University of Regensburg) · doi:10.5283/epub.31227 · Cited by 2
Casini, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821797-9.00003-9 · Cited by 6
Construction of Intelligent Nursing System Based on Visual Action Recognition Algorithm
Zeng, Y., & Bo, L. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8311371 · Cited by 2
RetractedSpace-Efficient Indexes for Uncertain Strings
Gabory, E., Liu, C., Loukides, G., et al. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00367 · Cited by 3
“While indexing standard strings for online pattern searches can be performed in linear time and space, indexing uncertain strings is much more challenging.”
BackgroundGorenflo, C., Golab, L., & Keshav, S. (2017) · International Conference on Scientific and Statistical Database Management · doi:10.1145/3085504.3085505 · Cited by 2
How to Discover Knowledge for Improving Availability in the Manufacturing Domain?
Utz, F., Neumann, C., & Omid, T. (2018) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2018.552 · Cited by 2
“To eliminate noise and reconstruct missing values, regression models and probabilistic models as well as outlier detection methods are applied [26].”
BackgroundMethodSecurity, Privacy, and Ethical Issues in Smart Sensor Health and Well-Being Applications
Sliwa, J. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812130-6.00007-x · Cited by 2
Medical Quality of Service Optimization Over Internet of Multimedia Things
Sodhro, A. H., Sangaiah, A. K., Sodhro, G. H., et al. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813314-9.00013-x · Cited by 2
Distributed Backup K-Placement and Applications to Virtual Memory in Wireless Networks
Oren, G., & Barenboim, L. (2020) · doi:10.1145/3427477.3429466 · Cited by 2
Big Data Scalability, Methods and its Implications
Amudhavel, J., Dananjayan, S., Raghav, R. S., et al. (2015) · doi:10.1145/2743065.2743121 · Cited by 1
Digital Technologies and Dynamic Resource Management
Bakker, K., Knight, R. J., Leape, J., Mackworth, A. K., Ng, R. T., & Ritts, M. (2020) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp50058.2020.00079 · Cited by 1
“Advances in edge computing now enable “smart” sensors to handle vast amounts of data with limited power/storage [72].”
BackgroundChaouche, A., Ilié, J., Hebik, A., et al. (2023) · Computing and Informatics · doi:10.31577/cai_2023_3_716 · Cited by 1
Physics-aware dual-branch architectures for accurate weather predictions
Zhang, C., & Zhou, W. (2025) · GeoInformatica · doi:10.1007/s10707-025-00537-z · Cited by 1
Assisted Sustainability: How Digital Technologies Promote Corporate Sustainability
Schrade-Grytsenko, L., Kappler, K. E., & Smolnik, S. (2025) · Sustainability · doi:10.3390/su17125561 · Cited by 2
Efficient $\eta$-Threshold Maintenance in Dynamic Uncertain Graphs
Chen, Y., Liu, Q., Zhu, Y., et al. (2025) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde65448.2025.00045 · Cited by 1
Identifying High-Number-Cluster Structures in RFID Ski Lift Gates Entrance Data
Delibašić, B., & Obradović, Z. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0038-8 · Cited by 1
“[1]).”
Cache-Based Periodic Query Optimization for Wireless Sensor Networks
Zhao, D., Zhou, Z., Ning, K., et al. (2014) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki.2014.51 · Cited by 1
Measuring the Uncertainty of RFID Location Streams Based on Optimal Estimation Particle Filter
Liu, Y., Han, F., Xu, X., et al. (2015) · International Journal of Distributed Sensor Networks · doi:10.1155/2015/758391 · Cited by 1
“RFID (Radio Frequency Identification) technology now is widely used in real-time location-based applications, due to the impact of the sensor itself and the surrounding environment, resulting in a large number of uncertain RFID location streams in these applications [1–3].”
BackgroundModeling the Process of Event Sequence Data Generated for Working Condition Diagnosis
Ding, J., Liu, Y., Zhang, L., & Wang, J. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/693450 · Cited by 1
Datenqualität – eine Managementaufgabe
Otto, B., & Österle, H. (2015) · Springer eBooks · doi:10.1007/978-3-662-46806-7_1 · Cited by 3
Event Data Analysis in Large Virtualized Environment
Bharath, M. B., & Ashoka, D. V. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-9059-2_28 · Cited by 1
Saade, S. D., López, M. D. L. A. G., Penalver, L. E. N., et al. (2018) · IEEE Biennial Congress of Argentina (ARGENCON) · doi:10.1109/argencon.2018.8645983 · Cited by 1
“[6] C.”
The Most Likely Object to be Seen Through a Window
Carmi, P., Chanchary, F., Maheshwari, A., et al. (2019) · International Journal of Computational Geometry & Applications · doi:10.1142/s0218195919500092 · Cited by 1
Adhikari, A., & Adhikari, J. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-13212-9_17 · Cited by 1
Data visualization techniques for the identification of missing values in dairy farming
Rachah, A., Løvendahl, P., Reksen, O., et al. (2024) · AIP conference proceedings · doi:10.1063/5.0195158 · Cited by 1
Sensor Data Cleaning Using Particle Swarm Optimization
Narkhede, P., Deshpande, S. D., & Walambe, R. A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16681-6_18 · Cited by 1
Linear Approximation Based Compression Algorithms Efficiency to Compress Environmental Data Sets
Väänänen, O., Zolotukhin, M., & Hämäläinen, T. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44038-1_11 · Cited by 1
A Computational Framework for Generalized Constrained Inverse Problems
O'Leary, P. L., & Ninevski, D. (2022) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps51978.2022.9816954 · Cited by 1
Anomaly Detection in Disaster Recovery: A Review, Current Trends and New Perspectives
Anand, P., Sharma, M., & Saroliya, A. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10072941 · Cited by 1
“We then speak about gaps in the existing literature and spotlight capability destiny studies instructions [14].”
BackgroundJoint Source Selection and Data Extrapolation in Social Sensing for Disaster ResponsePreprint
Hosseini, M., Nagibolhosseini, N., Bar-Noy, A., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.00500 · Cited by 1
“A survey on analytic challenges in the field recently appeared [Aggarwal and Abdelzaher 2013].”
BackgroundZastron, C. M. (2016) · SUNScholar (Stellenbosch University) · Cited by 1
Preety, S., & Kpereobong, L. (2021) · doi:10.1002/9781119711148.ch14 · Cited by 1
EASD - Experimental Algorithmics for Streaming Data
Bartz–Beielstein, T. (2016) · Cologne Open Science (RFH - University of Applied Sciences Cologne) · Cited by 1
Event detection in connected environments
Mansour, E. (2019) · theses.fr (ABES) · doi:10.70675/6b9470a5za607z445cz8dd1z698750c828b5 · Cited by 1
Monitoring Query Processing in Mobile Robot Databases
Sugiura, K., Hayashi, A., Dong, T., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43984-5_20
Statistical Models for Querying and Managing Time-Series Data
Sathe, S. K. (2013) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-5705
Gayathri, S., Kamaraj, P., & Arthanareeswari, M. (2014) · International Journal of Multidisciplinary and Current Research
A Method for Complex Event Detection of Out-of-Order RFID Data Flow
Ding, Y., Xu, H., Wang, R., et al. (2015) · International Workshop on Materials Engineering and Computer Sciences (IWMECS) · doi:10.2991/iwmecs-15.2015.40
“Complex event processing[4] (CEP) is a technology for processing large amount of raw data generated by RFID system, which combines a number of events into complex and obtains meaningful value.”
BackgroundApplication of Complex Event Processing Techniques to Big Data Related to Healthcare
Mohamedali, F., & Oussena, S. (2016) · Advances in business information systems and analytics book series · doi:10.4018/978-1-5225-0293-7.ch012
PROGRESSIVE DATA ANALYTICS IN HEALTH INFORMATICS USING AMAZON ELASTIC MAPREDUCE (EMR)
Mohan, J. S. S., & Shanmugapriya, P. (2016) · ICTACT Journal on Soft Computing · doi:10.21917/ijsc.2016.0168
Anomaly detection from time-changing environmental sensor data streams
Mamun, A.-A. (2016) · Memorial University Research Repository (Memorial University)
Mining Association Rules Events over Data Streams
Mourtada, A. (2017) · Spectrum Research Repository (Concordia University)
Hardware Acceleration of Frequent Itemsets Mining on Data Streams
Bustio-Martínez, L., Cumplido-Parra, R., Hernández-León, R., et al. (2014) · Research in Computing Science · doi:10.13053/rcs-71-1-2
Mobile RFID based Traceability for Tracking Seeds and Fuzzy Logic Applications
Seema, D., & Geetha, E. (2014) · International Conference on Information and Communication Technologies
A Study of Tourist Sequential Activity Patterns through Location Based Social Network (LBSN)Preprint
Talpur, A., & Zhang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03426
“The latter is crucial in the analysis of the tourist activity patterns in a given region or place [26].”
BackgroundModel driven root cause analysis and reliability enhancement for large distributed computing systems
Zasadziński, M. (2018) · doi:10.5821/dissertation-2117-123526
“Besides, Aggarwal [14] states the problem of having incomplete data transmitted from sensors and the 10 significance of this problem for Big Data analytics.”
BackgroundHuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor NetworksPreprint
Chereshnev, R., & Kertész‐Farkas, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08506
“The increasing availability of wearable body sensors leads to novel scientific studies and industrial applications [1].”
BackgroundMonitoring Daily Routine Anomalies in Assisted Living Environments
Technische Universität Dresden, D., Franke, M., Keller, I., et al. (2014) · University of Technology, Sydney eBooks · doi:10.5130/aad.a
“In detail we search for correlations with Principal Component Analysis [1] and afterwards use a Ranker to order the correlation.”
MethodChanchary, F. (2019) · doi:10.22215/etd/2019-13545
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 370
Selection of Information Streams in Social Sensing
Gianini, G., Mio, C., Viola, F., et al. (2020) · International Conference on Management of Digital EcoSystems (MEDES) · doi:10.1145/3415958.3433099
Streaming Data through the IoT via Actor-Based Semantic Routing Trees
Giouroukis, D., Jestram, J., Zeuch, S., et al. (2021) · HAL (Le Centre pour la Communication Scientifique Directe)
(2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800867-6.09992-1
A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases
Ke, S., Gong, J., Li, S., et al. (2021) · doi:10.32920/14638896
A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases
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Graceful Forgetting II. Data as a ProcessPreprint
Alain De Cheveigné, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.15441
“There is an abundant literature on changepoint detection within data streams (e.g. Aggarwal 2013), and more generally segmentation and clustering.”
BackgroundMethodThe Role of Knowledge Extraction from Big Data for Enhancing Healthcare Services
(2022) · International Journal of Advances in Computer Science and Technology · doi:10.30534/ijacst/2022/0111122022
The use of the clustering method for anomaly detection in the operation of a pumping unit
Lvov, V., & Echkina, E. (2023) · AIP conference proceedings · doi:10.1063/5.0163711
Data Mining and It's Applications in Healthcare
Kumari, P., Kaur, B., Rakhra, M., Singh, A. P., & Rawat, A. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10397856
Automatic Optimization of Tolerance Ranges for Model-Driven Runtime State Identification
Sint, S., Mazak‐Huemer, A., Eisenberg, M., Waghubinger, D., & Wimmer, M. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3386313
“We assume that inaccuracies (e.g., based on measurement uncertainty) exist in the sensor readings and/or recordings, as is often the case in practice [19].”
Secured Healthwatch Monitoring Solution Based on Quantum Computing
Ananthi, S., Singh, E. K., Prakash, M. K., Vishnu, M. J. S., Raj, D. G., & Jebastin, B. F. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-3601-4.ch023
Tang, B., & Zhu, F. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625502688
A Semantic Digital Twin Framework for Predictive Failure Analytics in IoT
Ramesh, P. K., Koorapati, K., Veeraswamy, S., et al. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3692807
Developments of Manufacturing Systems with a Focus on Product and Process Quality
Wuest, T. (2015) · Springer theses · doi:10.1007/978-3-319-17611-6_2
“Researchers look into various methods to identify and eliminate negative effects in sensor data, ranging from ML to Hopfield nets (Aggarwal 2013).”
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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