Multidimensional Indexing Methods Research Articles

Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework

Accurate, reliable, and stable streamflow forecasts are essential for risk assessment and decision making in water resources management. Owing to the limited value of deterministic forecasting, probabilistic forecasting incorporating probability and confidence intervals is achieved by optimizing the predictors and forecast equation while identifying forecast error features. The combination of deterministic forecasts and error identification can facilitate water resources management. This study improved the traditional forecast model’s inputs selection, structure designation, and error parameters calibration to propose a long-term probabilistic streamflow forecast model with hierarchical optimization of the predictors, forecast equation, and errors characteristics. The model develops information entropy theory to screen the driving predictor set, the long short-term memory (LSTM) model to conduct deterministic forecasts, and the generalized autoregressive conditional heteroskedasticity (GARCH) model to identify time-varying errors. The proposed model, used in some case studies to forecast the monthly streamflow of two lakes, Hongze and Luoma lakes in China, was evaluated using the multidimensional index method considering “accuracy–reliability–stability” performance. The results revealed the following: (1) Predictor screening based on information entropy investigates the statistical characteristics of predictors, thereby improving the reliability of streamflow forecasts. (2) The LSTM model exploits the response between the driving predictors and streamflow, whereas GARCH model identifies the time-varying features of forecast errors effectively, which reduces the probability of forecast failure and increases the accuracy and stability of probabilistic forecasts. (3) Under current meteorological observation conditions and forecasting capability, the proposed forecasts can extend the maximum forecast lead time from 1 month to 3 months. (4) The proposed model improves the accuracy (root mean square error: 6.7%–34.8%), reliability (Brier score: 15.3%–27.9%), and stability (mistaken distance: 36.4–52.6%) in comparison with the benchmark of the two case studies, indicating that “inputs–structure–parameters” hierarchical optimization provides effective forecast information for water resources management.

Multidimensional Measurement of Poverty Among Employed Women in Antalya

The aim of this study is to identify the poverty levels of “employed women” living in 5 central districts of Antalya and are among the disadvantaged groups with a measurement method that includes social dimensions. Poverty is measured by using the Alkire-Foster (multidimensional poverty index) method rather than monetary indicators such as incomeconsumption expenditure and by taking socioeconomic indicators such as education, health, and the physical structure of the household into account. To put it another way, the multidimensional poverty approach eliminates the deficiencies of monetary poverty measures in explaining welfare. For this purpose, a field study conducted through the face-toface survey method with 400 employed and married women living in Antalya in September 2020 forms the basis for the poverty analysis. 27 indicators representing the dimensions of the socioeconomic structure, employment, income, health, empowerment, social assistance, migration, physical security, and inclusion, without feeling embarrassed, are used in order to reveal the multidimensional poverty of working women. According to the findings of the study, income and employment are the dimensions in which women in Antalya experience the most deprivation. From this perspective, it is clear that income, particularly employment, should be prioritized in order to alleviate women’s poverty in this city. For example, policies aimed at increasing women’s educational attainment (positive discrimination) may make it easier for them to enter the labor market and, as a result, change their standing at work. Furthermore, expanding their economic liberties can help them enhance their socioeconomic level and gain access to better living conditions.

Improving multidimensional point query search using multi-way peer-to-peer tree network

Nowadays, peer-to-peer (P2P) networks are widely accepted in multidimensional applications like social networking, multiplayer games, P2P e-learning, P2P mobile ad-hoc networks, etc. Various P2P overlay networks combining multidimensional indexing (MI) methods are preferable for efficient multidimensional point or range search in a distributed environment. However, point query search in existing P2P has limitations viz.: 1) either does not give support to MI or uses replications to support MI; 2) point query search cost is limited to O(log2N). Hence, traditional MI techniques based on the multi-way tree structure (having larger fan-out) can be employed to enhance the multidimensional point query search capabilities. Based on our observations, a hybrid model combining m-ary (m fan-out of the tree, > 2) P2P tree network and MI based on space containment relationships is preferred to reduce point query search performance bound to O(logmN) using single overlay network. The present paper shows how this model improves the search performance of the point queries in O(logmN) steps, independent of the dimensionality of objects.

Improving multidimensional point query search using multi-way peer-to-peer tree network

Nowadays, peer-to-peer (P2P) networks are widely accepted in multidimensional applications like social networking, multiplayer games, P2P e-learning, P2P mobile ad-hoc networks, etc. Various P2P overlay networks combining multidimensional indexing (MI) methods are preferable for efficient multidimensional point or range search in a distributed environment. However, point query search in existing P2P has limitations viz.: 1) either does not give support to MI or uses replications to support MI; 2) point query search cost is limited to O(log2N). Hence, traditional MI techniques based on the multi-way tree structure (having larger fan-out) can be employed to enhance the multidimensional point query search capabilities. Based on our observations, a hybrid model combining m-ary (m fan-out of the tree, > 2) P2P tree network and MI based on space containment relationships is preferred to reduce point query search performance bound to O(logmN) using single overlay network. The present paper shows how this model improves the search performance of the point queries in O(logmN) steps, independent of the dimensionality of objects.

A New Indexing Method for Uncertain Databases

This paper presents an indexing method called the uncertain data index (UD-index) for uncertain databases. The design objectives of the UD-index are improving the range query performance of the multidimensional indexing methods and providing a compromise between optimal index node clustering. Although more than ten years of database research has resulted in a great variety of multidimensional indexing methods, most efforts have focused on the data-level clustering and there has been no attempt to cluster index nodes themselves in dynamic environments. As a result, most related index nodes are widely scattered on the disk due to dynamic page allocation, and it requires many random disk accesses during the range search. The UD-index avoids that by storing the related nodes contiguously in a segment that contains a sequence of contiguous disk pages. The UD-index improves the range query performance by offering high-performance sequential disk access within a segment. A new cost model is introduced to estimate the range query performance. It takes into consideration the physical adjacency of pages read as well as the number of pages accessed. The analytic performance analysis indicates that the UD-index shows better performance than the traditional indexing methods in most cases.

Dynamics of granzyme B-induced apoptosis: Mathematical modeling

Apoptosis is mediated by an intracellular biochemical system that mainly includes proteins (procaspases, caspases, inhibitors, Bcl-2 protein family as well as substances released from mitochondrial intermembrane space). The dynamics of caspase activation and target cleavage in apoptosis induced by granzyme B in a single K562 cell was studied using a mathematical model of the dynamics of granzyme B-induced apoptosis developed in this work. Also the first application of optimization approach to determination of unknown kinetic constants of biochemical apoptotic reactions was presented. The optimization approach involves solving of two problems: direct and inverse. Solving the direct optimization problem, we obtain the initial (baseline) concentrations of procaspases for known kinetic constants through conditional minimization of a cost function based on the principle of minimum protein consumption by the apoptosis system. The inverse optimization problem is aimed at determination of unknown kinetic constants of apoptotic biochemical reactions proceeding from the condition that the optimal concentrations of procaspases resulting from the solution of the direct optimization problem coincide with the observed ones, that is, those determined by biochemical methods. The Multidimensional Index Method was used to perform numerical solution of the inverse optimization problem.

A Data-Driven Multidimensional Indexing Method for Data Mining in Astrophysical Databases

Large archives and digital sky surveys with dimensions of bytes currently exist, while in the near future they will reach sizes of the order of . Numerical simulations are also producing comparable volumes of information. Data mining tools are needed for information extraction from such large datasets. In this work, we propose a multidimensional indexing method, based on a static R-tree data structure, to efficiently query and mine large astrophysical datasets. We follow a top-down construction method, called VAMSplit, which recursively splits the dataset on a near median element along the dimension with maximum variance. The obtained index partitions the dataset into nonoverlapping bounding boxes, with volumes proportional to the local data density. Finally, we show an application of this method for the detection of point sources from a gamma-ray photon list.

The Segment-Page Indexing Method for Large Multidimensional Queries

This paper presents a new indexing method called segment-page indexing (SP-indexing). The design objectives of SP-indexing are 2-fold: (i) improving the range query performance of multidimensional indexing methods and (ii) providing a compromise between optimal index clustering and excessive full index reorganization overhead. Although more than 10 years of database research has resulted in a great variety of multidimensional indexing methods, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes (i.e. disk pages) are widely scattered on a disk, and thus many random disk accesses are required during query processing. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves query performance by offering sequential access within a segment. A cost model is also introduced to estimate the performance of SP-indexing taking into account the physical adjacency of pages read, as well as the number of pages accessed. The analytic performance analysis indicates that SP-indexing shows a better performance than traditional indexing methods in most cases. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional methods using small disk pages with respect to total elapsed time.

A fast content-based indexing and retrieval technique by the shape information in large image database

In this paper, we present an efficient content-based image retrieval (CBIR) system which employs the shape information of images to facilitate the retrieval process. For efficient feature extraction, we extract the shape feature of images automatically using edge detection and wavelet transform which is widely used in digital signal processing and image compression. To facilitate speedy retrieval, we also propose the Spherical Pyramid-Technique (SPY-TEC), a new indexing method for similarity search in high-dimensional data space. The SPY-TEC is based on a special space partitioning strategy, which is to divide the d-dimensional data space first into 2 d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space. Thus, we are able to use a B +-tree to manage the transformed 1-dimensional data. We show that our image indexing method supports faster retrieval than other multi-dimensional indexing methods such as the R *-tree through various experiments. Finally, we show the retrieval effectiveness of our CBIR system using the measure proposed by the QBIC (C. Faloutsos et al., 1994) system.

Application of multidimensional indexing methods to massive processing of multimedia information

Since reduction of the processing cost is essential to large-scale multimedia applications, the processing technique for database systems can play an important role in massive processing of multimedia information. This paper focuses on the effectiveness of two multidimensional indexing methods, the index construction method and the set-based nearest-neighbor search method. First, this paper presents a static construction method of the Sphere/Rectangle-tree and evaluates its performance by applying it to the similarity retrieval of still images. Second, the set-based nearest-neighbor search method is presented, which is effective for the matching of feature vector sets. In order to evaluate its effectiveness and performance, we apply it to face sequence matching. The evaluation result demonstrates its scalability as well as its effectiveness. © 2000 Scripta Technica, Syst Comp Jpn, 31(13): 31–41, 2000

Application of multidimensional indexing methods to massive processing of multimedia information

Since reduction of the processing cost is essential to large-scale multimedia applications, the processing technique for database systems can play an important role in massive processing of multimedia information. This paper focuses on the effectiveness of two multidimensional indexing methods, the index construction method and the set-based nearest-neighbor search method. First, this paper presents a static construction method of the Sphere/Rectangle-tree and evaluates its performance by applying it to the similarity retrieval of still images. Second, the set-based nearest-neighbor search method is presented, which is effective for the matching of feature vector sets. In order to evaluate its effectiveness and performance, we apply it to face sequence matching. The evaluation result demonstrates its scalability as well as its effectiveness. © 2000 Scripta Technica, Syst Comp Jpn, 31(13): 31–41, 2000