New Range and k-NN Query Processing Algorithms Using Materialization Technique on Spatial Network

2007 ◽  
Author(s):  
Jung-Ho Um ◽  
Nihad Karim Chowdhury ◽  
Jae-Woo Chang
Keyword(s):  
Query Processing ◽  
Spatial Network ◽  
Processing Algorithms

scholarly journals Towards crowd-aware indoor path planning

2021 ◽  
Vol 14 (8) ◽  
pp. 1365-1377
Author(s):  
Tiantian Liu ◽  
Huan Li ◽  
Hua Lu ◽  
Muhammad Aamir Cheema ◽  
Lidan Shou
Keyword(s):  
Path Planning ◽  
Query Processing ◽  
Travel Time ◽  
Real Data ◽  
Experimental Results ◽  
Search Process ◽  
Unified Framework ◽  
Approximate Query Processing ◽  
Approximate Query ◽  
Processing Algorithms

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

Ad-hoc aggregate query processing algorithms based on bit-store for query intensive applications in cloud computing

2013 ◽  
Vol 29 (7) ◽  
pp. 1725-1735 ◽  
Author(s):  
Donghua Yang ◽  
Yuqiang Feng ◽  
Ye Yuan ◽  
Xixian Han ◽  
Jinbao Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Query Processing ◽  
Ad Hoc ◽  
Aggregate Query ◽  
Processing Algorithms

Models and Techniques for Approximate Queries in OLAP

2011 ◽  
pp. 2665-2670
Author(s):  
Alfredo Cuzzocrea
Keyword(s):  
Decision Support ◽  
Query Processing ◽  
Trend Analysis ◽  
Computational Cost ◽  
The Other ◽  
Qualitative Trend Analysis ◽  
Aggregate Queries ◽  
Processing Algorithms ◽  
Approximate Answers ◽  
Customer Services

Since the size of the underlying data warehouse server (DWS) is usually very large, response time needed for computing queries is the main issue in decision support systems (DSS). Business analysis is the main application field in the context of DSS, as well as OLAP queries being the most useful ones; in fact, these queries allow us to support different kinds of analysis based on a multi-resolution and a multi-dimensional view of the data. By performing OLAP queries, business analysts can efficiently extract summarized knowledge, by means of SQL aggregation operators, from very large repositories of data like those stored in massive DWSs. Then, the extracted knowledge is exploited to support decisions in strategic fields of the target business, thus efficiently taking advantage from the amenity of exploring and mining massive data via OLAP technologies. The negative aspect of such an approach is just represented by the size of the data, which is enormous, currently being tera-bytes and peta-bytes the typical orders of data magnitude for enterprise DWSs, and, as a consequence, data processing costs are explosive. Despite the complexity and the resource-intensiveness of processing OLAP queries against massive DWSs, client-side systems performing OLAP and data mining, the most common application interfaces versus DWSs, are often characterized by small amount of memory, small computational capability, and customized tools with interactive, graphical user interface supporting qualitative, trend analysis. For instance, consider the context of retail systems. Here, managers and analysts are very often more interested in the product-sale plot in a fixed time window rather than to know the sale of a particular product in a particular day of the year. In others words, managers and analysts are more interested in the trend analysis rather than in the punctual, quantitative analysis, which is, indeed, more proper for OLTP systems. This consideration makes it more convent and efficient to compute approximate answers rather than exact answers. In fact, typical decision-support queries can be very resource intensive in terms of spatial and temporal computational needs. Obviously, the other issue that must be faced is the accuracy of the answers, as providing fast and totally wrong answers is deleterious. All considering, the key is proving fast, exploratory answers with some guarantees on their degree of approximation. On the other hand, in the last few years, DSS have become very popular: for example, sales transaction databases, call detail repositories, customer services historical data, and so forth. As a consequence, providing fast, even if approximate, answers to aggregate queries has become a tight requirement to make DSS-based applications efficient, and, thus, has been addressed in research in the vest of the so-called approximate query answering (AQA) techniques. Furthermore, in such data warehousing environments, executing multi-steps, query-processing algorithms is particularly hard because the computational cost for accessing multi-dimensional data would be enormous. Therefore, the most important issues for enabling DSS-based applications are: (1) minimizing the time complexity of query processing algorithms by decreasing the number of the needed disk I/Os, and (2) ensuring the quality of the approximate answers with respect to the exact ones by providing some guarantees on the accuracy of the approximation. Nevertheless, proposals existent in literature devote little attention to the point (2), which is indeed critical for the investigated context.

Emergence Index in Image Databases

2011 ◽  
pp. 1361-1365
Author(s):  
Sagarmay Deb
Keyword(s):  
Image Retrieval ◽  
Query Processing ◽  
Database Management ◽  
Image Representation ◽  
Spatial Relations ◽  
Image Databases ◽  
Multimedia Databases ◽  
Processing Algorithms ◽  
Image Representations ◽  
High Level

Images are generated everywhere from various sources. It could be satellite pictures, biomedical, scientific, entertainment, sports and many more, generated through video camera, ordinary camera, x-ray machine, and so on. These images are stored in image databases. Content-based image retrieval (CBIR) technique is being applied to access these vast volumes of images from databases efficiently. Some of the areas, where CBIR is applied, include weather forecasting, scientific database management, art galleries, law enforcement, and fashion design. Initially image representation was based on various attributes of the image like height, length, angle and was accessed using those attributes extracted manually and managed within the framework of conventional database management systems. Queries are specified using these attributes. This entails a high-level of image abstraction (Chen, Li & Wang, 2004). Also there was feature-based object-recognition approach where the process was automated to extract images based on color, shape, texture, and spatial relations among various objects of the image. Recently combining these two approaches, efficient image representation and query-processing algorithms, have been developed to access image databases. Recent CBIR research tries to combine both of these above mentioned approach and has given rise to efficient image representations and data models, query-processing algorithms, intelligent query interfaces and domain-independent system architecture. As we mentioned, image retrieval can be based on lowlevel visual features such as color (Antani, Rodney Long & Thoma, 2004; Deb & Kulkarni, 2007; Deb & Kulkarni, 2007a; Ritter & Cooper, 2007; Srisuk & Kurutach, 2002; Sural, Qian & Pramanik, 2002; Traina, Traina, Jr., Bueno, & Chino, 2003; Verma & Kulkarni, 2004), texture (Antani et al., 2004; Deb & Kulkarni, 2007a; Zhou, Feng & Shi, 2001), shape (Ritter & Cooper, 2007; Safar, Shahabi & Sun, 2000; Shahabi & Safar, 1999; Tao & Grosky, 1999), high-level semantics (Forsyth et al., 1996), or both (Zhao & Grosky, 2001). But most of the works done so far are based on the analysis of explicit meanings of images. But image has implicit meanings as well, which give more and different meanings than only explicit analysis provides. In this paper we provide the concepts of emergence index and analysis of the implicit meanings of the image which we believe should be taken into account in analysis of images of image or multimedia databases.

Sign in / Sign up

Export Citation Format

Share Document