Processing of extreme moving-object update and query workloads in main memory

Sidlauskas, Darius; Šaltenis, Simonas; Jensen, Christian S.

doi:10.1007/s00778-014-0353-2

Cited by 15 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is, however, only applicable for mesh spatial datasets as it relies on the mesh connectivity to retrieve query results. The parallel implementation of a recently proposed moving object join [38] also uses a uniform spatial grid to index the locations of objects. The separation of the grid cells is exploited to use multiple threads to either update or join the data simultaneously.…”

Section: Joining Moving Objectsmentioning

confidence: 99%

Thermal-Join

Tauheed¹,

Heinis

Ailamaki

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

Simulations have become ubiquitous in many domains of science. Today scientists study natural phenomena by first building massive three-dimensional spatial models and then by simulating the models at discrete intervals of time to mimic the behavior of natural phenomena. One frequently occurring challenge during simulations is the repeated computation of spatial self-joins of the model at each simulation time step. The join is performed to access a group of neighboring spatial objects (groups of particles, molecules or cosmological objects) so that scientists can calculate the cumulative effect (like gravitational force) on an object.Computing a self-join even in memory, soon becomes a performance bottleneck in simulation applications. The problem becomes even worse as scientists continue to improve the precision of simulations by increasing the number as well as the size (3D extent) of the objects. This leads to an exponential increase in join selectivity that challenges the performance and scalability of state-of-the-art approaches.We propose THERMAL-JOIN, a novel spatial self-join algorithm for dynamic memory-resident workloads. The algorithm groups objects in spatial proximity together into hot spots. Hot spots minimize the cost of computing join as objects assigned to a hot spot are guaranteed to overlap with each other. Using a nested spatial grid, THERMAL-JOIN partitions and indexes the dataset to locate hot spots. With experiments we show that our approach provides a speedup between 8 to 12× compared to the state of the art and also scales as scientists improve the precision of their simulations.

show abstract

Section: Joining Moving Objectsmentioning

confidence: 99%

Thermal-Join

Tauheed¹,

Heinis

Ailamaki

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

show abstract

“…People conduct most of their day-to-day activities in indoor environments, such as entertaining, living, working, and shopping. Studies have shown that people spend around 80% of their lives indoors [2,[5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Vertical Indexing for Moving Objects in Multifloor Environments

Alamri

Taniar

Nguyen

2018

Mobile Information Systems

View full text Add to dashboard Cite

e indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. ere are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects moving in indoor space. In this paper, we extend the C-tree to solve another interesting problem. It can be observed that many indoor spaces (such as shopping centers) contain wings/sections. For such a space, there are queries for which the wing/section location of an object, rather than the cellular location, is the relevant answer (e.g., "the object is in the east wing"). In this paper, we propose a new index structure, called the GMI-tree ("GMI" stands for "Graph-based Multidimensional Index"). e GMI-tree is based on two notions of distance, or equivalently, two notions of adjacency: one represents horizontal adjacency and the other represents vertical adjacency.

show abstract

“…As the update frequency is growing, the workloads are also increasing. High workloads in Sidlauskas's paper can't be managed by traditional disk-based techniques [2]. Rather, we propose a primary memory index that is compatible with the characteristic of parallelism accessible in multicore processors.…”

Section: Introductionmentioning

confidence: 99%