Relevant bibliographies by topics / Time Series DBMS

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Time Series DBMS'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Time Series DBMS"

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Fischer, Ulrike, Lars Dannecker, Laurynas Siksnys, Frank Rosenthal, Matthias Boehm, and Wolfgang Lehner. "Towards Integrated Data Analytics: Time Series Forecasting in DBMS." Datenbank-Spektrum 13, no. 1 (October 20, 2012): 45–53. http://dx.doi.org/10.1007/s13222-012-0108-4.

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Liu, Haicheng, Peter van Oosterom, Theo Tijssen, Tom Commandeur, and Wen Wang. "Managing large multidimensional hydrologic datasets: A case study comparing NetCDF and SciDB." Journal of Hydroinformatics 20, no. 5 (May 10, 2018): 1058–70. http://dx.doi.org/10.2166/hydro.2018.136.

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Abstract Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a large multidimensional (MD) dataset is stored in the NetCDF classic or the 64-bit offset format. The essence of this issue lies in the contiguous storage structure adopted by NetCDF. In this research, NetCDF file-based solutions and a MD array database management system applying a chunked storage structure are benchmarked to determine the best solution for storing and querying large MD hydrologic datasets. Expert consultancy was conducted to establish benchmark sets, with the HydroNET-4 system being utilized to provide the benchmark environment. In the final benchmark tests, the effect of data storage configurations, elaborating chunk size, dimension order (spatio-temporal clustering) and compression on the query performance, is explored. Results indicate that for big hydrologic MD data management, the properly chunked NetCDF-4 solution without compression is, in general, more efficient than the SciDB DBMS. However, benefits of a DBMS should not be neglected, for example, the integration with other data types, smart caching strategies, transaction support, scalability, and out-of-the-box support for parallelization.
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Luu, Do Ngoc, Nguyen Ngoc Phien, and Duong Tuan Anh. "Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.

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There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.
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Farhan, Ahmad, Yeni Sumantri, and Purnama Budi Santoso. "Rancangan Sistem Informasi Berbasis Web Untuk Mengatasi Perbaikan Mesin Menggunakan Group Tecnologhy." JAMI: Jurnal Ahli Muda Indonesia 1, no. 2 (December 31, 2020): 53–61. http://dx.doi.org/10.46510/jami.v1i2.30.

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Abstrak Objektif. Dunia memasuki revolusi industri 4.0 yang ditandai dengan era digitalisasi. Digitalisasi juga merambah ke ilmu perawatan mesin, salah satunya adalah dalam hal troubleshooting (TS). TS adalah serangkaian tindakan yang diperlukan untuk menangani kerusakan yang terjadi pada mesin. Permasalahan yang sering timbul pada TS adalah: teknisi belum tentu ada dilokasi setiap saat, buku manual TS suatu mesin masih berupa buku cetak yang berisiko : buku hilang, buku keselip, dan letak buku jauh dari lokasi kerusakan mesin. Riset ini berusaha mengatasi permasalahan ini dengan cara mengembangkan aplikasi yang disebut Sistem Informasi Troubleshooting Mesin Produksi (SITMEP). Metode. Metode penelitian untuk membuat SITMEP dikembangkan dengan cara mengintegrasikan beberapa ilmu seperti: Group Technology (GT) untuk membantu merancang arsitektur database SITMEP, database relasional untuk membangun sistem database SITMEP, dan bahasa Structured Query Language (SQL) untuk mempercepat pencarian cara TS yang tepat. Hasil. Sebagai hasil penelitian adalah arsitekstur SITMEP dalam bentuk Entity Relation Diagram (ERD). Atas dasar ERD ini dapat dikembangkan menjadi sistem basis data SITMEP. Selanjutnya dikembangkan statmen SQL untuk mempermudah proses pencarian TS komponen yang rusak. Dengan menggunakan database management system (DBMS) dan Ms.Access 2016. Kesimpulan. SITMEP dapat di tempatkan di website perusahaan yang menggunakannya. Aplikasi SITMEP diletakkan di web agar dapat diakses oleh siapa saja, kapan saja, dan dimana saja. Tools yang digunakan adalah Microsoft Acces beserta Visual Basic for Application (VBA) adapun langkah selanjutnya aplikasi dapat digunakan dan dikembangkan di banyak mesin industri. Abstrack Objective. The world entered the 4.0 industrial revolution marked by the era of digitalization. Digitalisation also extends to the science of machine maintenance, one of which is in terms of troubleshooting (TS). TS is a series of actions needed to deal with damage that occurs in the engine. Problems that often arise in the TS are: technicians are not necessarily in the location all the time, the TS manual of a machine is still a printed book at risk: books are missing, books are slipped, and the books are far from the location of damage to the machine. This research seeks to overcome this problem by developing an application called the Production Machine Troubleshooting Information System (SITMEP). Methods. The research method to create SITMEP was developed by integrating a number of sciences such as: Group Technology (GT) to help design the SITMEP database architecture, relational databases to build a SITMEP database system, and Structured Query Language (SQL) language to speed up the search for the right TS method. Results. As a result of the research is the architecture of SITMEP in the form of Entity Relations Diagram (ERD). On the basis of this ERD it can be developed into a SITMEP database system. Then the SQL statement was developed to simplify the process of finding TS damaged components. By using a database management system (DBMS) and Ms.Access 2016. Conclusion. SITMEP can be placed on the website of the company that uses it. The SITMEP application is placed on the web so that it can be accessed by anyone, anytime, and anywhere. The tools used are Microsoft Access along with Visual Basic for Application (VBA). As for the next step, the application can be used and developed in many industrial machines.
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Park, Keun-Tae, and Jun-Geol Baek. "Time Series Prediction using ARIMA and DBNs with MODWT." Journal of the Korean Institute of Industrial Engineers 43, no. 6 (December 31, 2017): 474–81. http://dx.doi.org/10.7232/jkiie.2017.43.6.474.

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Zenati, Athmen, and Yang-Kyoo Han. "Synthesis and characteristics of novel azo-based diblock copolymers and their self-assembly behavior via solvents and thermal annealing." e-Polymers 17, no. 6 (October 26, 2017): 523–35. http://dx.doi.org/10.1515/epoly-2017-0042.

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AbstractA series of azo-based diblock copolymers (DBCs) with various compositions were successfully synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization in anisole with PCAEMA-CTA (macro-CTA), DOPAM (new acrylamide monomer) and AIBN (initiator). Kinetic studies on diblock copolymerization manifested a controlled/living manner with good molecular weight control. Structures and properties of monomers and DBCs were determined by 1H nuclear magnetic resonance (NMR), differential scanning calorimetry (DSC) and gel permeation chromatography (GPC). Liquid crystalline (LC) phases and morphological properties were investigated using optical polarizing microscope (OPM), atomic force microscopy (AFM), scanning electron microscopy (SEM) and small-angle X-ray scattering (SAXS). Experimental results demonstrated that the prepared PCAEMA-CTA and DBCs possessed low polydispersity index (≤1.37). All DBCs revealed sharp endothermic transition peaks corresponding to the smectic-to-nematic phase. DBCs with high azo contents showed batonnet textures of the smectic phase whereas DBCs of low azo segments displayed threaded textures of the nematic phase. DBC with 49 wt% of azo side-chains generated a lamellar compared to DBCs with low azo block (≤41 wt%) or non-azo block (≤38 wt%) which produced hexagonal-type nanostructures. In addition, all DBCs exhibited reversible trans-cis photoisomerization behavior under UV irradiation and dark storage at different intervals of time.
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Ortega-Hernandez, Alejandro, Raphael Acayaba, Chad Verwold, Cassiana Carolina Montagner, and Susana Y. Kimura. "Emerging investigator series: emerging disinfection by-product quantification method for wastewater reuse: trace level assessment using tandem mass spectrometry." Environmental Science: Water Research & Technology 7, no. 2 (2021): 285–97. http://dx.doi.org/10.1039/d0ew00947d.

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A sensitive analytical method was developed that simultaneously characterizes 25 unregulated DBPs in wastewater effluents. DBP formation potential with HOCl and NH2Cl was evaluated across a full-scale wastewater reuse facility for the first time.
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Rowan, D. M., M. A. Tucker, B. J. Shappee, and J. J. Hermes. "Detections and constraints on white dwarf variability from time-series GALEX observations." Monthly Notices of the Royal Astronomical Society 486, no. 4 (April 20, 2019): 4574–89. http://dx.doi.org/10.1093/mnras/stz1116.

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Abstract We search for photometric variability in more than 23 000 known and candidate white dwarfs (WDs), the largest ultraviolet survey compiled for a single study of WDs. We use gphoton, a publicly available calibration/reduction pipeline, to generate time-series photometry of WDs observed by GALEX. By implementing a system of weighted metrics, we select sources with variability due to pulsations and eclipses. Although GALEX observations have short baselines (≤30 min), we identify intrinsic variability in sources as faint as Gaia G = 20 mag. With our ranking algorithm, we identify 48 new variable WDs in archival GALEX observations. We detect 40 new pulsators: 36 have hydrogen-dominated atmospheres (DAVs), including one possible massive DAV, and four are helium-dominated pulsators (DBVs). We also detect eight new eclipsing systems; five are new discoveries, and three were previously known spectroscopic binaries. We perform synthetic injections of the light curve of WD 1145+017, a system with known transiting debris, to test our ability to recover similar systems. We find that the 3σ maximum occurrence rate of WD 1145+017-like transiting objects is $\le 0.5{{\ \rm per\ cent}}$.
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Zhang, Jiao, Yanhui Wang, and Dezhen Wang. "Period Multiplication in a Continuous Time Series of Radio-Frequency DBDs at Atmospheric Pressure." Communications in Computational Physics 11, no. 4 (April 2012): 1226–35. http://dx.doi.org/10.4208/cicp.150710.051110s.

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AbstractAs a spatially extended dissipative system with strong nonlinearity the radio-frequency (rf) dielectric-barrier discharges (DBDs) at atmospheric pressure possess complex spatiotemporal nonlinear behaviors. In this paper, the time-domain nonlinear behaviors of rf DBD in atmospheric argon are studied numerically by a one-dimensional fluid model. Simulation results show that, under appropriate controlling parameters, the rf DBD can undergo a transition from single-period state to chaos through period doubling bifurcation with increasing discharge time, i.e., the regular periodic oscillation and chaos can coexist in a long time series of the atmospheric-pressure rf DBD. With increasing applied voltage amplitude, the duration of the periodic oscillation reduces gradually and chaotic zone increases, and finally the whole discharge series becomes completely chaotic state. This is different from conventional period doubling route to chaos. Moreover, the spatial characteristics of rf period-doubling discharge and chaos, as well as the parameter range of various discharge behaviors occurring are also investigated in this paper.
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Chen, Junfei, Qiongji Jin, and Jing Chao. "Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/235929.

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With the global climate change, drought disasters occur frequently. Drought prediction is an important content for drought disaster management, planning and management of water resource systems of a river basin. In this study, a short-term drought prediction model based on deep belief networks (DBNs) is proposed to predict the time series of different time-scale standardized precipitation index (SPI). The DBN model is applied to predict the drought time series in the Huaihe River Basin, China. Compared with BP neural network, the DBN-based drought prediction model has shown better predictive skills than the BP neural network for the different time-scale SPI. This research can improve drought prediction technology and be helpful for water resources managers and decision makers in managing drought disasters.
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Dissertations / Theses on the topic "Time Series DBMS"

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Warrén, Linus, and Daniel Tallkvist. "Time Series databaser för sensorsystem : En experimentell studie av prestanda för Time Series databaser för sensorsystem som grundas på: NoSQL eller RDBMS." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-43763.

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Purpose – The purpose of this study is to recommend a database and its belonging database model which is optimized for a sensor system. There is a lack of comparisons for databases and data models for bigger sensor systems. The study also brings scientific support for whom wishes to build a sensor system like the one which is included in this paper. Method – This paper starts with a literature study, which purpose is to choose the databases and the database models to be included in the comparison. To achieve the purpose of the study, a quantitative approach has been chosen. The study follows the steps that defines an experimental study within software development according to Shari Lawrence Pfleeger. Four predefined cases are used to compare the databases and the different database models which has been obtained in the literature study. Findings – The literature study shows that Time Series DBMS is the recommended database model to use for implementing sensor systems. The findings of the study also show that TimescaleDB is the preferable database over InfluxDB in four of four predefined cases. The null hypothesis which has been admitted is rejected and the alternative hypothesis is accepted at 1% significance level. Implications – The implications of the paper is to enhance the knowledge about Time Series DBMS, specifically of TimescaleDB and InfluxDB for sensor systems. The result can be implemented and used when resembling sensor systems are created. According to the result of the experiment it is shown that TimescaleDB is better than InfluxDB for sensor systems with similar datastructure. Limitations – Two Time Series DBMS (TimescaleDB and InfluxDB) were used in the experiments in this paper. The experiments was is carried out in Azure and is limited to 10 vCPU:s that a standard account have access to. There were not many beacons available to use for creating testdata. Files with corresponding data that the beacon sends out was created to simulate beacons. Keywords – Time Series DBMS, NoSQL, RDBMS, TimescaleDB, InfluxDB, Sensor systems
Syfte – I problembeskrivningen framgår att det finns brist på vetenskapligt underlag för vilken sorts databas som är optimal att använda för ett sensorsystem. Det saknas jämförelser av prestanda mellan olika databaser och datamodeller i större sensorsystem. Studiens syfte är: ”Att rekommendera en databas och tillhörande databasmodell som är optimerad för ett sensorsystem” Metod – Studien inleds med en litteraturstudie för att genom teorin välja databas och databasmodeller som ska ingå i studien. För att uppnå syftet har en kvantitativ ansats valts. Studien följer de steg som Shari Lawrence Pfleeger definierar som en experimentell studie inom mjukvaruutveckling. Fyra fördefinierade fall används för att jämföra databaserna med olika databasmodeller som erhållits i litteraturstudien. Resultat - Litteraturstudien visar att Time Series DBMS är den databasmodell som rekommenderas att användas i ett sensorsystem. Studiens resultat visar att TimescaleDB presterar bättre än InfluxDB i fyra av fyra fördefinierade fall. Nollhypotesen som har ställts upp förkastas och en mothypotes antas vid 1% signifikansnivå. Implikationer - Studiens implikationer är att öka och fylla vissa kunskapshål kring Time Series DBMS, specifikt TimescaleDB och InfluxDB för sensorsystem. Resultatet kan tillämpas och användas när liknande sensorsystem skall implementeras. Enligt experimentets resultat visar det att TimescaleDB är bättre än InfluxDB för sensorsystem med liknande struktur. Begränsningar – Två Time Series DBMS (TimescaleDB och InfluxDB) ingår i denna studie som experimenten utfördes på. Experimenten utföres i Azure och var begränsade av de 10 vCPU:erna ett standardkonto har tillgång till att använda. Det fanns inte tillgång till ett stort antal beacons för att generera data till experimenten, så filer med motsvarande data skapades för att simulera beacons.  Nyckelord - Time Series DBMS, NoSQL, RDBMS, TimescaleDB, InfluxDB, Sensorsystem
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Battaglia, Bruno. "Studio e valutazione di database management system per la gestione di serie temporali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17270/.

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La tesi è incentrata sulle time series e la loro gestione. Dopo aver esposto cosa fosse una serie temporale ed alcuni casi di utilizzo, la dissertazione prosegue elencando le famiglie di DBMS ed i criteri attraverso i quali valutarli. Successivamente si è descritto il modello che ogni DBMS implementava e, dopo aver dato un accenno di esso, si è passati alle tecniche usate per la gestione e l'analisi delle serie temporali. Ancora dopo, invece, si sono viste le tecniche di modellazione di un database in grado di gestire serie storiche e sono stati analizzati tutti i DBMS presi in esame attraverso i criteri sopracitati. Una comparazione, anche tramite forma tabellare, è stata accompagnata da una descrizione che potesse guidare il lettore ad una comprensione rapida delle differenze, dei punti di forza e delle debolezze di ogni TSDB. Infine sono state tratte le conclusioni che, in seguito al percorso svolto, sono sembrate più appropriate, sono stati individuati dei punti chiave su cui incentrare i lavori futuri e sono stati proposti altri spunti di lavoro ai quali non si è potuto lavorare per mancanza di ulteriore tempo e di disponibilità dei software completi di tutte le loro funzionalità.
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Book chapters on the topic "Time Series DBMS"

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Young, Peter C. "Data-Based Mechanistic (DBM) Modelling." In Recursive Estimation and Time-Series Analysis, 357–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21981-8_12.

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Enright, Catherine G., Michael G. Madden, Niall Madden, and John G. Laffey. "Clinical Time Series Data Analysis Using Mathematical Models and DBNs." In Artificial Intelligence in Medicine, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22218-4_20.

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Santos, Ricardo Jorge, Jorge Bernardino, and Marco Vieira. "Using Data Masking for Balancing Security and Performance in Data Warehousing." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, 384–409. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch015.

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Data Warehouses (DWs) are the core of sensitive business information, which makes them an appealing target. Encryption solutions are accepted as the best way to ensure strong security in data confidentiality while keeping high database performance. However, this work shows that they introduce massive storage space and performance overheads to a magnitude that makes them unfeasible for DWs. This work proposes a data masking technique for protecting sensitive business data in DWs which balances security strength with database performance, using a Formula based on the mathematical modular operator and simple arithmetic operations. The proposed solution provides apparent randomness in the generation and distribution of the masked values, while introducing small storage space and query execution time overheads. It also enables a false data injection method for misleading attackers and increasing the overall security strength. It can be easily implemented in any DataBase Management System (DBMS) and transparently used, without changes to application source code. Experimental evaluations using a real-world DW and TPC-H decision support benchmark implemented in leading DBMS Oracle 11g and Microsoft SQL Server 2008 demonstrate its overall effectiveness. Results show the substantial savings of its implementation costs when compared with state of the art encryption solutions provided by those DBMS and that it outperforms those solutions in both data querying and insertion of new data.
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Diao, Qian, Jianye Lu, Wei Hu, Yimin Zhang, and Gary Bradski. "DBN Models for Visual Tracking and Prediction." In Bayesian Network Technologies, 176–93. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-141-4.ch009.

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In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.
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Khan, Latifur, Dennis McLeod, and Cyrus Shahabi. "An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases." In Human Computer Interaction Development & Management, 93–116. IGI Global, 2002. http://dx.doi.org/10.4018/978-1-931777-13-1.ch006.

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An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems, which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same DBMS connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. Furthermore, we extend our mechanism with an adaptive technique that detects sub-optimality of a plan during query execution and attempts to switch to the cheapest plan while avoiding redundant work and imposing little overhead. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.
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Conference papers on the topic "Time Series DBMS"

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Telnarova, Zdenka. "Time series patterns and language support in DBMS." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992236.

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Lee, Doyup. "Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.0-126.

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Kim, Jaein, Chorwon Kim, Byunghee Son, Jihyoung Ryu, and Sungchang Kim. "A study on Time-series DBMS Application for EdgeX-based lightweight edge gateway." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289173.

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Kuremoto, Takashi, Masanao Obayashi, Kunikazu Kobayashi, Takaomi Hirata, and Shingo Mabu. "Forecast chaotic time series data by DBNs." In 2014 7th International Congress on Image and Signal Processing (CISP). IEEE, 2014. http://dx.doi.org/10.1109/cisp.2014.7003950.

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Chen, Leitao, Laura Schaefer, and Xiaofeng Cai. "An Accurate Unstructured Finite Volume Discrete Boltzmann Method." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87136.

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Unlike the conventional lattice Boltzmann method (LBM), the discrete Boltzmann method (DBM) is Eulerian in nature and decouples the discretization of particle velocity space from configuration space and time space, which allows the use of an unstructured grid to exactly capture complex boundary geometries. A discrete Boltzmann model that solves the discrete Boltzmann equation (DBE) with the finite volume method (FVM) on a triangular unstructured grid is developed. The accuracy of the model is improved with the proposed high-order flux schemes and interpolation scheme. The boundary treatment for commonly used boundary conditions is also formulated. A series of problems with both periodic and non-periodic boundaries are simulated. The results show that the new model can significantly reduce numerical viscosity.
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Journal articles
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