Designing Access Methods for Bitemporal Databases

By supporting the valid and transaction time dimensions, bitemporal databases represent reality more accurately than conventional databases. In this paper we examine the issues involved in designing efficient access methods for bitemporal databases and propose the partial-persistence and the double-tree methodologies. The partial- persistence methodology reduces bitemporal queries to partial persistence problems for which an efficient access method is then designed. The double-tree methodology "sees" each bitemporal data object as consisting of two intervals (a valid-time and a transaction- time interval), and divides objects into two categories according to whether the right endpoint of the transaction time interval is already known. A common characteristic of both methodologies is that they take into account the properties of each time dimension. Their performance is compared with a straightforward approach that "sees" the intervals associated with a bitemporal object as composing one rectangle which is stored in a single multidimensional access method. Given that some limited additional space is available, our experimental results show that the partial- persistence methodology provides the best overall performance, especially for transaction timeslice queries. For those applications that require ready, off-the-shelf, access methods the double-tree methodology is a good alternative. (Also cross-referenced as UMIACS-TR-97-24

Bitemporal Sliding Windows

The bitemporal data model associates two time intervals with each record - system time and application time - denoting the validity of the record from the perspective of the database and of the real world, respectively. One issue that has not yet been addressed is how to efficiently answer sliding window queries in this model. In this work, we propose and experimentally evaluate a main-memory index called BiSW that supports sliding windows on system time, application time, and both time attributes simultaneously. Our experimental results show that BiSW outperforms existing approaches in terms of space footprint, maintenance overhead and query performance

High-Dimensional Spatio-Temporal Indexing

There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. However, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional space. Furthermore, most high-dimensional indexing methods try to reduce the number of dimensions in the data being indexed and compress the information given by all dimensions into few dimensions but are not able to store now - relative data. One of the most efficient high-dimensional indexing methods, the Pyramid Technique, is able to handle high-dimensional point-data only. Nonetheless, we take this technique and extend it such that it is able to handle spatio-temporal data as well. We introduce a technique for querying in this structure with spatio-temporal queries. We compare our technique, the Spatio-Temporal Pyramid Adapter (STPA), to the RST-tree for in-memory and on-disk applications. We show that for high dimensions, the extra query-cost for reducing the dimensionality in the Pyramid Technique is clearly exceeded by the rising query-cost in the RST-tree. Concluding, we address the main drawbacks and advantages of our technique

Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella

The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested. In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%. Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models. In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for large-area biomass or stem volume mapping. Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plot-level forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS based and TerraSAR X based features was smaller than in any previous study in which ALS and different kinds of SAR materials have been compared. In this thesis, forest mapping and monitoring applications using active 3D RS were developed. Spatially accurate 3D RS enables the mapping of harvesting sites, the monitoring of changes in the canopy structure and even the making of a fully RS-based forest inventory. ALS is carried out at relatively low altitudes, which makes it relatively expensive per area unit, and other RS materials are still needed. Spaceborne stereo radargrammetry proved to be a promising technique to acquire additional 3D RS data efficiently as long as an accurate digital terrain model is available as a ground-surface reference.Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella. Metsävaroista kerätään mahdollisimman tarkkaa tietoa metsänomistajan päätöksenteon tueksi. Tietoa kerätään puustotunnusten lisäksi toimenpidekohteista ja metsässä tapahtuvista muutoksista, kuten kasvusta ja luonnontuhoista. Laajojen metsäalueiden kartoituksessa käytetään apuna lentokoneesta tai satelliiteista tehtävää kaukokartoitusta. Metsien kaukokartoitus on viime vuosina ottanut merkittävän kehitysaskeleen, kun aktiiviset 3D-kaukokartoitusmenetelmät ovat yleistyneet. Aktiivisessa kaukokartoituksessa, kuten laserkeilauksessa ja tutkakuvauksessa instrumentti vastaanottaa lähettämäänsä säteilyä. Laserkeilaus tuottaa kohteesta 3D-havaintoja, jotka metsäalueilla kuvaavat suoraan puuston pituutta ja metsän tiheyttä. Laserkeilauksella kohteesta saadaan tällä hetkellä tyypillisesti 0,5−20 havaintoa/m2. Laserkeilaus tehdään lentokoneesta 500−3000 m korkeudesta, jolloin aineiston hankinta laajoilta alueilta on kallista verrattuna satelliittikuviin. Myös satelliittitutkakuvilta voidaan tuottaa spatiaalisesti tarkkaa 3D-tietoa, jonka pistetiheys on tosin huomattavasti harvempaa kuin laserkeilauksella. Tutkimuksessa kehitettiin sovelluksia metsien kartoitukseen ja seurantaan hyödyntäen aktiivisia 3D-kaukokartoitusmenetelmiä. Metsiköiden toimenpidetarvetta ennustettiin onnistuneesti laserkeilausaineiston avulla. Harvennettaviksi luokitellut metsiköt pystyttiin kartoittamaan 70%−86% tarkkuudella. Kahden ajankohdan laserkeilausaineistoja käytettiin lumituhojen vuoksi vaurioituneiden puiden kartoittamiseen. Tuhoutuneen latvuspinta-alan kartoitus perustui laserkeilausaineistosta tuotettujen latvusmallien erotuskuviin. Kehitetty menetelmä soveltuu latvusrakenteessa tapahtuneiden muutosten, kuten lumi- ja tuulituhojen, kartoittamiseen ja seurantaan. Laajojen metsäalueiden kartoitus perustuu yleensä kaksivaiheeseen inventointimenetelmään, jossa käytetään maastomittauksia ja tiedon yleistyksessä kaukokartoitusaineistoa. Kartoitusta voidaan tehostaa joko maastomittauksia vähentämällä tai hyödyntämällä mahdollisimman halpaa kaukokartoitusaineistoa. Tutkimuksessa kehitettiin täysin kaukokartoitukseen perustuva kaksivaiheinen metsien inventointimenetelmä. Tarvittava maastotieto mitattiin suoraan laserkeilausaineistosta. Menetelmä soveltuu puuston tilavuuden tai biomassan kartoitukseen erityisesti alueille, joilla maastomittausten kustannukset ovat merkittävät. Satelliittitutkakuvat ovat potentiaalinen aineisto etenkin laajojen alueiden metsävarojen seurannassa. Synteettisen apertuurin tutka (SAR)-stereokuvilta mitattiin automaattisesti 3D-pisteitä, joita käytettiin puustotunnusten ennustamisessa. Keskitilavuus ennustettiin parhaimmillaan lähes samalla tarkkuudella kuin laserkeilauksella. Tutkimus osoitti aktiivisen 3D-kaukokartoitustiedon mahdollistavan entistä yksityiskohtaisemman metsien kartoituksen ja seurannan

A spatiotemporal indexing method for disaggregate transportation data

Time, location, and attributes are three elements of a GIS, but all commercial GIS packages can only handle location and attributes; they are in fact a static GIS. Spatiotemporal GIS has been a hot research topic recentlySpatiotemporal GIS and its application in transportation research are still prematureThis thesis focuses on spatiotemporal query problems on travel data Specifically, It attempts to answer this question during a time period which trips pass through one or more specific streets? To speed up this spatiotemporal query for large data sets, a spatiotemporal index on the trip data is built by combining Avenue, AML, and C+. All the trip origin ends and those last destination ends for each individual on each day are geocoded using Avenue scripts The trip shortest path route system is created based on ArcInfo dynamic segmentation and network analysis functionsAn array of 2-D tree structures based on each trip\u27s beginning time and ending time and each street traversed are then created in C++ and AvenueThis array of 2-D tree structures is stored in memory. Finally, the spatiotemporal query function is performed by examining the array of 2-D tree structures for a given time window using Avenue and C++. A sample trip log data file in the Knoxville Metropolitan Area and Knox county street shape file are used to implement the spatiotemporal query. This thesis is concluded that efficient indexing methods must be developed to handle complicated spatiotemporal queries for large travel data set

High-dimensional spatio-temporal indexing

There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. How- ever, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional space. Furthermore, most high-dimensional indexing methods try to reduce the number of dimensions in the data being indexed and compress the information given by all dimensions into few dimensions but are not able to store now - relative data. One of the most efficient high-dimensional indexing methods, the Pyramid Technique, is able to handle high-dimensional point-data only. Nonetheless, we take this technique and extend it such that it is able to handle spatio-temporal data as well. We introduce a technique for querying in this structure with spatio-temporal queries. We compare our technique, the Spatio-Temporal Pyramid Adapter (STPA), to the RST-tree for in-memory and on-disk applications. We show that for high dimensions, the extra query-cost for reducing the dimensionality in the Pyramid Technique is clearly ex- ceeded by the rising query-cost in the RST-tree. Concluding, we address the main drawbacks and advantages of our technique