TrajMesa

Brief Introduction

TrajMesa is one of the most important module of our system JUST (JD Urban Spatio-Temporal Data Engine). It aims to manage big trajectory data efficiently in a convenient way. The framework of TrajMesa is shown as the following figure.

Key Features

(1) Scalability

TrajMesa can manage unduly large number of trajectories, but require little for the clusters.

(2) Efficiency

In our experimental settings, TrajMesa is sometimes 100~1000 times faster than the state-of-the-art in-memory trajectory management system.

(3) Plenty of Queries Support

TrajMesa supports various widely used trajectory queries, including but not limited to:

ID Temporal Query
Spatial Range Query
Similarity Query (for Fréchet distance, Hausdorff distance and DTW distance)
k-NN Query (k-NN point query and k-NN trajectory query)
Spatio-Temporal Range Query
Similarity Temporal Query
...

(4) Easy of Use

We design and realize a complete SQL engine, thus all trajectory preprocessing and query processing functions can be done with a SQL-like statement.

Experiments

Datasets

We use three trajectory datasets to evaluate the performance of TrajMesa.

* T-Drive taxi trajectory distribution in Beijing, China

* Lorry trajectory distribution in Guangzhou, China

Results

TrajMesa shows a competitive query efficiency with the-state-of-the-art trajectory data management systems (sometimes even 100~1000 times faster than the in-memory advanced frameworks), and is much more scalable than them.

Experimental Source Code & Technical Report

Experimental Source Code

Technical Report

System Demostration

We prepare a test account for the reviewers of TKDE. UserName: JustTestUser, Password: JustTestUser!1

Login

1. Enter the login page: http://portal-just.urban-computing.cn/login

2. Enter the User Name and Password, then click the button 登录, as shown in the following picture.

3. The user interface is shown as the following picture, which has four panels: Table Panel, View Panel, JustQL Panel, and Result Panel.

An Example of Trajectory Management

1. Create a table:


create table trajectory_table as trajectory 
userdata { 
    "geomesa.indices.enabled": "xz2,xz2t",
    "geomesa.z3.interval": "day",
    "geomesa.xz.precision": "16"
}

2. Load data from the HDFS:


LOAD hdfs:'/just_tutorial/trajectory_data' to just:trajectory_table 
Config {
    oid: "0",
    tid:"0",
    time: "to_timestamp(3)",
    geom: "st_makePoint(1,2)"
} separator ','

3. Spatial range query:


select 
    *, 
    item 
from 
    trajectory_table 
where 
    item within st_makeMBR(113.1,23.2,113.5,23.6)

Note that the item is a implicit field.

Users can show the result with a map view.

4. Spatial-temporal range query:


select
    *,
    item
from
    trajectory_table
where
    item within st_makeMBR(113.1, 23.2, 113.5, 23.6)
    and item between '2014-03-01 00:00:00' and '2014-03-15 00:00:00'

5. K-NN point query:


select
    *,
    item
from
    trajectory_table
where
    item in st_KNN(st_makePoint(113.84, 22.63), 100)

6. K-NN trajectory query:


select
    *,
    item
from
    trajectory_table
where
    item in st_KNN(
        st_makeTrajectory(
            'LINESTRING (113.780632 22.689121, 113.780281 22.689474, 113.780319 22.690006, 113.780464 22.691366, 113.782349 22.691311)'
        ),
        'frechet',
        2)

Now we support three widely used trajectory distance: frechet, hausdorff, dtw.

7. Create a view:


create view trajectory_view as
select
    *,
    item
from
    trajectory_table
where
    item within st_makeMBR(113.1, 23.2, 113.5, 23.6)

8. Trajectory noise filter:


SELECT
    st_trajNoiseFilter(
        item,
        { 
            "@type": "COMPLEX_FILTER",
            "maxSpeedMeterPerSecond": 60.0,
            "segmenterParams": { 
                "maxTimeIntervalInMinute": 60,
                "maxStayDistInMeter": 100,
                "minStayTimeInSecond": 100,
                "minTrajLengthInKM": 1,
                "segmenterType": "ST_DENSITY_SEGMENTER" 
            } 
        }
    )
FROM
    trajectory_view

Where the second param of st_trajNoiseFilter is to config which filter method is used. If omited, we will use the default method.

9. Trajectory segmentation:


SELECT
    st_trajSegmentation(
        item,
        { 
            "maxTimeIntervalInMinute": 10,
            "maxStayDistInMeter": 100,
            "minStayTimeInSecond": 100,
            "minTrajLengthInKM": 2,
            "segmenterType": "HYBRID_SEGMENTER" 
        }
)
FROM
    trajectory_view

Where the second param of st_trajSegmentation is to config which segmentation method is used. If omited, we will use the default method.

10. Trajectory stay point:


SELECT
    st_trajStayPoint(
        item,
        { 
            "maxStayDistInMeter": 15,
            "minStayTimeInSecond": 60,
            "stayPointType": "CLASSIC_DETECTOR" 
        }
    )
FROM
    trajectory_view

Where the second param of st_trajStayPoint is to config which stay point detection method is used. If omited, we will use the default method.

11. Store the stay point detection result:


create view stay_point_view as
SELECT
    st_trajStayPoint(
        item,
        { 
            "maxStayDistInMeter": 15,
            "minStayTimeInSecond": 60,
            "stayPointType": "CLASSIC_DETECTOR" 
        }
    )
FROM
    trajectory_view


store view stay_point_view to table stay_point_table

12. Drop views:


drop view trajectory_view


drop view stay_point_view

13. Drop tables:


drop table trajectory_table


drop table stay_point_table