Keyword coupling query of spatiotemporal data based on XML

2021 ◽  
pp. 1-10
Author(s):  
Luyi Bai ◽  
Zengmei Cui ◽  
Xinyi Duan ◽  
Hao Fu
Keyword(s):  
Time Series ◽  
Calculation Method ◽  
Spatiotemporal Data ◽  
Logical Relationship ◽  
Keyword Query ◽  
Data Representations ◽  
Data Revision ◽  
Best Parameter ◽  
Relationship Of ◽  
Logic Relationship

With the increasing popularity of XML for data representations, there is a lot of interest in keyword query on XML. Many algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to analyze the logical relationship between keywords of spatiotemporal data. To overcome this limitation, in this paper, we firstly propose the concept of query time series (QTS) according to the data revision degree. For the logical relationship of keywords in QTS, we study the intra-coupling logic relationship and the inter-coupling logic relationship separately. Then a calculation method of keyword similarity is proposed and the best parameter in the method is found through experiment. Finally, we compare this method with others. Experimental results show that our method is superior to previous approaches.

scholarly journals Implementasi Metode Fuzzy Time Series untuk Peramalan Jumlah Pengunjung di Benteng Fort Rotterdam

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
V. Vivianti ◽  
Muhammad Kasim Aidid ◽  
Muhammad Nusrang
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Time Series ◽  
Logical Relationship ◽  
Logic Relationship ◽  
Fuzzy Logical

Abstract, Peramalan merupakan kegiatan yang dilakukan untuk memprediksi nilai suatu variable di waktu yang akan datang. Tujuan dari penelitian ini adalah mengimplementasikan Metode Fuzzy Time Series untuk memprediksi jumlah Pengunjung Benteng Fort Rotterdam. Metode Fuzzy Time Series adalah sebuah metode peramalan yang menggunakan himpunan Fuzzy sebagai dasar dalam Proses prediksi. Tahapan Peramalan dalam penelitian ini adalah mendefinisikan semesta pembicaraan U, menentukan jumlah dan Panjang kelas interval, defuzzifikasi dan mendefenisikan himpunan Fuzzy pada U, melakukan Fuzzifikasi pada data jumlah pengunjung, menentukan Fuzzy logic relationship (FLR), membentuk Fuzzy Logical Relationship Group (FLRG), melakukan defuzzifikasi, dan melakukan perhitungan peramalan. Dalam meramalkan jumlah Pengunjung di Benteng Fort Rotterdam dengan menggunakan Metode Fuzzy Time Series diperoleh hasil peramalan sebanyak 16240,35 atau dibulatkan menjadi 16240 Pengunjung pada bulan selanjutnya, dengan nilai MAPE sebesar 119,93 dan RMSE sebesar 4739,08.Keywords: Fuzzy, Time Series, Peramalan, Fort Rotterdam

Study on the rule and logical relationship of TCM prescription for lumbago

2020 ◽  
Vol 24 (4) ◽  
pp. 368
Author(s):  
Yong Xiao ◽  
Jiaozhi Wang ◽  
Shaowu Shen ◽  
Xiaoqiong Wang ◽  
Yunfang Liu ◽  
...  
Keyword(s):  
Logical Relationship ◽  
Relationship Of

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals An analysis of the relationship of inflation and unemployment to the gross domestic product (gdp) in zimbabwe

2014 ◽  
Vol 1 (3) ◽  
pp. 156-162
Author(s):  
Tendai Makoni
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Cointegration Test ◽  
Domestic Product ◽  
Long Run ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Relationship Of ◽  
The Relationship ◽  
Yearly Data

The time series yearly data for Gross Domestic Product (GDP), inflation and unemployment from 1980 to 2012 was used in the study. First difference of the logged data became stationary as suggested by the time series plots. Johansen Maximum Likelihood Cointegration test indicated a long-run relationship among the variables. Granger Causality tests suggested unidirectional causality between inflation and GDP, implying that GDP is Granger caused by inflation in Zimbabwe. Another unidirectional causality was noted between unemployment and inflation. The causality between unemployment and inflation imply that unemployment do affect GDP indirectly since unemployment influences inflation which in turn positively affect GDP.

scholarly journals Time Series Modelling for the Relationship of Ambient Temperature and Foodborne Diseases

2006 ◽  
Vol 163 (suppl_11) ◽  
pp. S199-S199
Author(s):  
M Fleury ◽  
D Charron ◽  
J Holt ◽  
O B Allen ◽  
A Maarouf
Keyword(s):  
Time Series ◽  
Ambient Temperature ◽  
Foodborne Diseases ◽  
Time Series Modelling ◽  
Relationship Of ◽  
The Relationship

Based on LabVIEW Three-Stage Current Computer Protection Experimental System

2015 ◽  
Vol 734 ◽  
pp. 732-735
Author(s):  
You Wen Tian ◽  
Hua Song Zhao
Keyword(s):  
Data Acquisition ◽  
Fault Simulation ◽  
Experimental System ◽  
Simulation Software ◽  
Loop Structure ◽  
Logical Relationship ◽  
Teaching Platform ◽  
Data Acquisition Module ◽  
Relationship Of ◽  
Microcomputer Protection

Current microcomputer protection experimental system is created based on LabVIEW. Each module was created based on the modular thinking (data acquisition module, start module, algorithm module). Functions of microcomputer protection were implemented. Fault data was generated by fault simulation software MATLAB. LabVIEW controls and loop structure achieved simulating a variety of action logical relationship of computer protection. This system had many advantages, such as visibility, controllability, authenticity and flexibility. The system was able to verify the performance of computer protection at different fault. The system was also able to analyze the situation of computer protection maloperation and misoperation, thus it further provided teaching platform for the three section current microcomputer protection experiment.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

scholarly journals Forecasting Model for Stock Market Based on Probabilistic Linguistic Logical Relationship and Distance Measurement

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 954
Author(s):  
Aiwu Zhao ◽  
Junhong Gao ◽  
Hongjun Guan
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Exchange ◽  
Forecasting Model ◽  
Historical Time ◽  
Logical Relationship ◽  
Probabilistic Linguistic Term Set ◽  
Time Series Dataset ◽  
Proposed Model ◽  
Linguistic Term

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

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