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·Data aggregation is a crucial process in the world of data analysis enabling you to combine and summarize large volumes of data from diverse sources to gain meaningful insights and make informed decisions Spatial Aggregation Aggregate sales data by geographic regions or countries to identify top performing markets and potential areas for
·This paper introduces GeoSpark an in memory cluster computing framework for processing large scale spatial data that achieves better run time performance than its Hadoop based counterparts SpatialHadoop This paper introduces GeoSpark an in memory cluster computing framework for processing large scale spatial data GeoSpark consists of three
·This paper proposes Lagic; a cloud based solution scheme to process continuous spatial keyword range queries over moving objects Lagic is the first model that provides an exact solution to the problem and minimizes the overhead on users devices A parallelized in memory indexing structure is proposed to ensure the efficiency and scalability
We consider variations of the standard orthogonal range searching motivated by applications in database querying and VLSI layout processing In a generic instance of such a problem called a range aggregate query problem we wish to preprocess a set S of geometric objects such that given a query orthogonal range q a certain intersection or proximity query on the objects of S
·In order to handle spatial data efficiently as required in computer aided design and geo data applications a database system needs an index mechanism that will help it retrieve data items
·Currently a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications Resource Description Framework RDF is widely accepted for
Therefore our findings seem to reconcile the different theories showing a unitary system that is involved in spatial processing across a range of spatial scales while nevertheless having an internal organization according to scale Several factors might explain the shift in cortical activity when subjects make judgments at different scales
·The paper presents the details of designing and developing GeoSpark which extends the core engine of Apache Spark and SparkSQL to support spatial data types indexes and geometrical operations at scale The paper also gives a detailed analysis of the technical challenges and opportunities of extending Apache Spark to support state of the art spatial
·tions are vital for spatial analysis and spatial data mining Spatial range queries inquire about certain spatial objects exist in a certain area Return all parks in Phoenix Spatial join queries are queries that combine two datasets ormorewithaspatialpredicate suchasdistancerelations find the parks that have rivers in
·A Scalable Algorithm for Maximizing Range Sum in Spatial Databases DongWan Choi 1 ChinWan Chung 1 2 Yufei Tao2 3 1Department of Computer Science KAIST Daejeon Korea 2Division of Web Science and Technology KAIST Daejeon Korea 3Department of Computer Science and Engineering Chinese University of Hong Kong New Territories
·The study focuses on the data models query Language query processing indexes and query optimization of a spatial databases that approves spatial databases as a necessary tool for data storage
Our diverse range of aggregate processing equipment and quarry machinery makes us the ideal partner to help sand and aggregate operators produce more with less Our Processing Equipment The construction aggregates industry has a crucial role in supporting rising living standards and delivering the infrastructure needs of the world s growing
indexing approach PolyFit for processing approximate range aggregate queries Our contributions are summarized as follows To the best of our knowledge this is the rst study that learning based approach to the spatial domain with their learned Z order model that aims to support fast spatial indexing How ever there are two main di
·However there is a lack of research that investigates the effects of location privacy protection techniques on answering range aggregate queries which are a common way to use spatial data In our work we not only measure the privacy levels achieved by participants but also the performance of our approach in answering range aggregate queries 3
·To the best of our knowledge there is no research on spatial temporal range aggregation query in UAV networks In this paper we propose an Efficient Spatial Temporal range Aggregation query processing ESTA algorithm for UAV networks First a topology change graph is constructed based on the pre planned trajectory information
Large amount of uncertain data is inherent in many novel and important applications such as sensor data analysis and mobile data management A probabilistic threshold range aggregate PTRA query retrieves summarized information about the uncertain objects satisfying a range query with respect to a given probability threshold
·interesting and visually appealing areas of image processing Image enhancement approaches fall into two broad categories spatial domain methods and frequency domain term spatial domain refers to the image plane itself and approaches in this category are based on direct manipu lation of pixels in an image
·indexing approach PolyFit for processing approximate range aggregate queries Our contributions are summarized as follows •To the best of our knowledge this is the first study that utilizes polynomial functions to learn indexes that support approximate range aggregate queries •PolyFit supports multiple types of range aggregate queries
·System users can leverage the newly defined SRDDs to effectively develop spatial data processing programs in Spark The Spatial Query Processing Layer efficiently executes spatial query processing algorithms Spatial Range Join KNN query on SRDDs G eo S park also allows users to create a spatial index R tree
·indexing approach PolyFit for processing approximate range aggregate queries Our contributions are summarized as follows To the best of our knowledge this is the rst study that learning based approach to the spatial domain with their learned Z order model that aims to support fast spatial indexing How ever there are two main di
·The rapid popularization of high speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data STBD new dimensions due to
·Range aggregate queries find frequent application in data analytics In some use cases approximate results are preferred over accurate results if they can be computed rapidly and satisfy
·Score range Pain subscale 0 50 ASES points function/disability subscale 0 50 ASES points Total score 0 100 ASES points 0 = worse pain and functional loss/disability FG Cioffi DA Amadio PC Wright JG Caughlin B The American Academy of Orthopedic Surgeons Outcomes Instruments normative values from the general
·The 0/1 encoding technique transforms range comparisons into set intersections Therefore it successfully conceals the relationship between the upper/lower bound of a range query and the encrypted index To securely and effectively perform range query we design an encrypted GBF which protects the values in GBF from being leaked after query •
