發(fā)布時間：2018-06-25 15:10

  本文選題：NoSQL + 軌跡大數(shù)據(jù)��； 參考：《華東師范大學》2017年碩士論文

【摘要】：近年來,隨著GPS(Global Positioning System)定位技術(shù)以及互聯(lián)網(wǎng)的發(fā)展,位置數(shù)據(jù)的采集方式越來越多樣化,使得軌跡數(shù)據(jù)的數(shù)據(jù)量不斷增加。越來越多的基于位置服務(wù)(Location-Based Service)的平臺為我們的生產(chǎn)生活提供豐富的服務(wù),這些在線服務(wù)平臺需要具備較高的數(shù)據(jù)存取效率,利用傳統(tǒng)關(guān)系型地理數(shù)據(jù)庫管理海量軌跡數(shù)據(jù)己不能滿足實際需求。本文以船舶航行軌跡數(shù)據(jù)為研究對象,旨在提出一種使用NoSQL數(shù)據(jù)庫管理大規(guī)模軌跡數(shù)據(jù)的方案,設(shè)計軌跡數(shù)據(jù)的空間索引,提高軌跡時空查詢的效率。本文主要從以下三方面研究海量軌跡數(shù)據(jù)在NoSQL數(shù)據(jù)庫中的存儲及軌跡時空查詢。(1)在數(shù)據(jù)庫存儲架構(gòu)方面,采用Redis與LevelDB數(shù)據(jù)庫結(jié)合的方式管理軌跡數(shù)據(jù)。利用Redis數(shù)據(jù)寫入效率高而LevelDB讀取數(shù)據(jù)快的特點,將Redis數(shù)據(jù)庫作為數(shù)據(jù)的前段緩存數(shù)據(jù)庫,實時接收數(shù)據(jù)并存入內(nèi)存中,然后再從內(nèi)存中讀取數(shù)據(jù)并將數(shù)據(jù)持久化存儲在LevelDB數(shù)據(jù)庫中。利用這種存儲方案既能滿足海量軌跡數(shù)據(jù)高效存儲的需求又能將數(shù)據(jù)持久化存儲在磁盤中,降低數(shù)據(jù)管理成本。(2)在軌跡數(shù)據(jù)存儲模型設(shè)計上,利用鍵值型數(shù)據(jù)庫靈活的數(shù)據(jù)結(jié)構(gòu),采用有序集合形式存儲軌跡數(shù)據(jù)。將數(shù)據(jù)對象對應(yīng)的時間點以1h為間隔歸并時段,以對象標識符(船號)及歸并后的時段作為Key,Value對應(yīng)存儲該時段內(nèi)該條船的動態(tài)信息集合,動態(tài)信息以坐標對、地面航行速度、航向、旋轉(zhuǎn)速率等拼接字符串的形式表示。以Unix時間戳作為Score值,數(shù)據(jù)按照Score值進行排序。(3)在對空間索引的優(yōu)化方面,采用網(wǎng)格索引,以有序集合存儲索引信息。索引設(shè)計同時考慮軌跡數(shù)據(jù)的時空特性。以0.25°的間隔劃分經(jīng)緯網(wǎng)格,以網(wǎng)格左下角經(jīng)緯度作為網(wǎng)格號,將時間點按照1h的時間間隔歸并時段,Key由網(wǎng)格號和歸并后的時段組成,Value對應(yīng)這段時間內(nèi)出現(xiàn)在該網(wǎng)格內(nèi)的船號集合,Score值為相應(yīng)的Unix時間戳。進行時空查詢時,根據(jù)與查詢窗口相交的網(wǎng)格號及查詢條件中的時態(tài)信息,首先查詢索引信息,得到對應(yīng)的船號集合及相應(yīng)的時間點信息。再以船號及時段作為查詢條件,查詢軌跡數(shù)據(jù)有序集合,得到船的坐標對及相應(yīng)的時間點的集合,查詢結(jié)果按照Score值即時間點序列化,進一步得到船舶的運行軌跡。通過與傳統(tǒng)對象關(guān)系型地理數(shù)據(jù)庫Geodatabase進行數(shù)據(jù)存儲及查詢效率的對比,驗證了本文提出的數(shù)據(jù)存儲架構(gòu)能夠有效提高數(shù)據(jù)存取效率,軌跡數(shù)據(jù)集合存儲模型能夠有效減小數(shù)據(jù)冗余,降低數(shù)據(jù)存儲所需空間。包含時空信息的索引有效提高大規(guī)模軌跡數(shù)據(jù)管理及時空查詢效率。
[Abstract]:In recent years, with the GPS (Global Positioning System) positioning technology and the development of the Internet, the acquisition of location data is more and more diversified, making the data amount of the trajectory data increasing. More and more platform based services (Location-Based Service) provide a rich service for our production and life, these online services The service platform needs to have high efficiency of data access. Using the traditional relational geo database to manage the mass trajectory data has not met the actual needs. This paper takes the ship navigation trajectory data as the research object, and aims to propose a scheme to manage the large-scale trajectory data using the NoSQL database and design the spatial index of the trajectory data. The efficiency of high trajectory spatio-temporal query. This paper mainly studies the storage and trajectory spatio-temporal query of mass trajectory data in NoSQL database from the following three aspects. (1) in database storage architecture, using the combination of Redis and LevelDB database to manage trajectory data, using Redis data to write high efficiency and LevelDB to read the fast data The Redis database is used as the front cache database of the data, receiving data in real time and storing it in memory, then reading data from memory and storing data persisted in the LevelDB database. This storage scheme can not only satisfy the requirement of efficient storage of mass trajectory data but also store the data persisted in the disk. The cost of low data management. (2) in the design of the trajectory data storage model, using the flexible data structure of the key database and storing the track data in an orderly set form, the time point corresponding to the data object is divided into the interval of 1H as the interval, and the object identifier (ship number) and the time period after the merging are used as Key, and Value corresponds to the storage time period. The dynamic information set of the ship is expressed in the form of a stitching string, such as the coordinate pair, the speed of the ground, the course and the rotation rate. The Unix timestamp is used as the Score value, the data is sorted according to the Score value. (3) the grid index is used to store the index information in an orderly set. The spatial and temporal characteristics of the trajectory data are considered. The longitude and latitude grid is divided at 0.25 degrees, and the latitude and longitude of the lower left corner of the grid is used as the grid number. The time points are classified according to the time interval of the 1H. The Key is composed of the grid number and the time period after the merging. The Value corresponds to the set of ship numbers in the grid in this period, and the Score value is the corresponding Unix. During the time and space inquiry, in the time and space query, according to the grid number intersected with the query window and the temporal information in the query condition, first query the index information, get the corresponding set of ship number and the corresponding time point information. Then the ship number and time period are used as the query conditions to query the orderly collection of the trajectory data and get the coordinates of the ship and the corresponding time points. Set, the query result is serialized according to the Score value of time point, and further gets the running track of the ship. By comparing the data storage and query efficiency with the traditional object relational geo database Geodatabase, it is proved that the data storage architecture proposed in this paper can effectively raise the efficiency of the data access and the memory module of the trajectory data set. The model can effectively reduce data redundancy and reduce the space required for data storage. The index containing spatio-temporal information can effectively improve the management of large-scale trajectory data and the efficiency of spatio-temporal query.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13

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