Bitmap lattice index in road networks

Transcription

1 J. Cent. South Univ. (04) : DOI: 0.007/s y Bitmap lattice index in road networks Doohee Song, Keun-Ho Lee, Kwangjin Park. Information Communication Engineering, Wonkwang University, Iksan-shi, Korea;. Division of Information Communication Engineering, Baekseok University, Cheonan-shi, Korea Central South University Press and Springer-Verlag Berlin Heidelberg 04 Abstract: A novel technique called the bitmap lattice index (BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server s workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and in the Hilbert curve (HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to knn query, and the technique is assessed by a performance evaluation experiment. Key words: road network; wireless broadcast; spatial query; bitmap lattice index (BLI) Introduction The location-based service (LBS) provides consumers with a meaningful service by combining information based on users position and real-time web technology [ 3]. It used to be regarded as a service that mobile communications providers could offer, but currently, spatial query services are leading the market and being used in social network services (SNSs), location-based searches, and location-based advertisement [4]. The trend in location-based related technologies is Circle, one of the Geo-SNS (Geo is geographic abbreviation of applications), which provides a distance-based service and expands its user base using a free call feature. For example, Uber, a taxi reservation service company in the USA, provides a calling service using a smart phone. As mentioned above, the LBS combines location information secured by the global positioning system (GPS), mobile communication networks, and digitalized maps to provide useful geographic information for users. A server collects positioning information from a smart phone equipped with a GPS chip over a high-speed wireless network and uses this information to provide a variety of services such as phone navigation, traffic information, and finding friends. Generally, a server can use one of two methods, on-demand or wireless broadcasting, to provide clients with the LBS. The on-demand method is a one-to-one transmission mode in which uplinks and downlinks are both possible between servers and clients [5 6]. In the past, when a server processed a query using the on-demand method, it often experienced an excessive communication load as the number of query requests from clients increased. As the hardware performance of servers has been improved, query processing work is now faster. Nonetheless, when clients request a large number of queries at the same time in a dense space such as a sports stadium, a bottleneck can occur in a server s work. Because of this, a client may wait for a query result for several minutes to several hours. This increases the battery consumption of their phones in clients who request queries [7 9]. To solve this problem, we supported a road network-based spatial query processing using a wireless broadcast environment. Wireless broadcasting is a one-to-many transmission mode by which a server sends a client information [0 4]. Its main characteristic is scalability that can process queries effectively regarding a common issue from a large, unspecific number of clients within an effective server communication range. That is to say, regardless of the number of query requests from clients, a server can transmit query results to all clients within a constant time. A client can receive a query result by tuning to broadcasting related to query-related data Received date: 03 0; Accepted date: Corresponding author: Kwangjin Park; kjpark@wku.ac.kr

2 J. Cent. South Univ. (04) : selectively among the entire broadcasting cycle that a server transmits. Despite such advantages of wireless broadcasting, few studies have been done on spatial query processing based on the road network in a wireless broadcasting environment [5]. Therefore, in this work, we propose a novel technique called the bitmap lattice index (BLI), which supports spatial queries in the road network over a wireless broadcasting environment. Next, we describe how Euclidian space networks and road networks locate objects differently. Figure shows how the final result of finding an object between a Euclidian space network and a road network is different even if a client (C) and the object (O) exist in the same location. It is easy for aircraft or ships to utilize the Euclidian distance [6]. However, most clients move on foot or use vehicles, so our spatial query processing takes the road network environment into consideration. When a client processes a spatial query over an existing road network, the client receives information from adjacent clients [7 9]. However, if a search area increases, a client needs to check all adjacent clients, increasing the number of query messages and the query processing time. To solve this problem, our proposed technique processes a spatial query in a road network based on wireless broadcasting. The main contributions of this work are as follows: ) Using the connection information between a bitmap of a node in the BLI and a node, the position and distance of an object from a query point can be identified with a small amount of data. ) Spatial query processing over an existing road network processes queries by sending/receiving information between adjacent clients. However, when a search area increases, duplicated query messages increase, resulting in the congestion of messages. We propose a method of reducing unnecessary data transmission and listening by sending information over a wireless broadcasting environment rather than using a message exchange method between clients. 3) The advantages of the proposed technique are assessed through cost models and performance evaluation. System model The terms used frequently in this work are summarized in Table. Table Definition of relevant terms Term Server Client Object Index Node Edge Definition Processing all information related to objects that exist on a map and sending query results requested from clients A terminal of a user who requests a query Result values clients want to obtain (e.g., detailed information on a location or prices of petroleum stations or restaurants) Information that is generated in advance and helps users to use data more efficiently in searching spatial information Vertex made to measure straight distance of a road A line connecting nodes b n b o C i,j,k,,n Bitmap of a node Bitmap of an object Connection information between nodes Fig. Network kinds of spatial query processing: (a) Euclidean space network; (b) Road network The configuration method used for the system s model is divided into two stages: First, the proposed technique divides a map through the HC and configures a map to be positioned in each grid. A node s location within the map can be identified via the node s bitmap. Once the locations of all nodes within the map are inserted, the nodes are connected through connection information between nodes. Algorithm : Roadmap construction method Input: HC order (N), node s bitmap (b n ), connection information between nodes (C i,j,k,,n) Output: node s location, connection information between nodes and node s roadmap HC order check followed by map division

3 3858 Reading the bitmap of a node followed by the designation of the node s location 3 for (i>=n) 4 N i ={C i,j,,x} 5 N j = {C j,k,,x} N n ={C n,,x} 8 return road map Previous researchers have processed queries by providing coordinate information on each node, connection information between nodes, and distance information, which leads to the storage of a substantial amount of information. On the contrary, the BLI only uses information on nodes connections and locations so it can calculate nodes distance using less data. The calculation equation for distance is as follows: d ( Ni, N j ) ( Nix N jx ) ( Niy N jy ) () where d(n i, N j ) refers to a distance between node i and node j, while N ix refers to the x axis of node i and N iy refers to the y axis of node i. Figure shows a roadmap configured through connection information between nodes and node s bitmap. The roadmap, which is information received from a server, is stored in a client via queue or stack structure [0]. J. Cent. South Univ. (04) : Fig. 3 Roadmap configured through a object s bitmap The index structure is divided into a header file and a main index, as shown in Fig. 4. First, a map is divided according to the HC order of the header file. Then, the index number m, which is inserted in every cycle of broadcasting, is checked. Second, the main index stores each node s bitmap regarding each grid divided in a map, connection information between nodes, and each object s bitmap. b n provides the location information of each node that can exist in a grid, which makes nodes coordinate information unnecessary. C i,j,,n is information on how each node is connected. As shown in Fig., N 3 is connected to N 4 and N 5. That is to say, in C 3,4,5, the main node is 3 and connected minor nodes are 4 and 5. As such, using the information of C i,j,k...,n, a node s information regarding its connection to other nodes can be checked and connected. Therefore, using the node s location within a map and connection information between nodes, the distance between nodes can be checked. Figure 3 shows the location of an object within a map configured using an object s bitmap and insertion to each grid. Even without distance information between nodes, between nodes and objects, between query points and nodes, and between objects, required in previous studies, the proposed technique can identify a distance through distance information between nodes and between objects and nodes calculated beforehand. Fig. Roadmap configured through a node s bitmap and connection information between nodes Second, once all information regarding nodes within a map via the first method is stored, objects locations are sequentially identified through their bitmaps, as shown in Fig. 3. That is to say, a map can be configured through the HC order, connection information between nodes and nodes as well as objects bitmaps. Fig. 4 Structure of index (header and main index)

4 J. Cent. South Univ. (04) : Related work 3. Road network based spatial query processing Studies based on existing road networks have used solution-based approaches and extended spatial database approaches [7 8, 3]. The former had a pointer to the next coming node and distances of all nodes; thus, it had the disadvantage of taking a considerable amount of time to calculate them in advance [4 5]. The latter required all adjacent nodes around a node to be checked, resulting in a large I/O process [9]. Reference [6] aimed to solve the weaknesses presented in the previous studies. ROAD in Ref. [6] performed spatial query processing by checking the minimum distance [7] of a node in a road network environment and grouping the nodes to reduce the search space. The query processing method aimed to reduce a node s search range using a depth-first method once a search area was grouped via the hierarchical structure called R net. This technique can reduce overhead from indexing and updates. However, in this work, the researchers did not apply this technique to a wireless broadcasting environment. When techniques in Ref. [6] were applied to a wireless broadcasting environment to send data, backtracking occurred due to the R-tree structure [8]. Backtracking is a phenomenon that occurs when finding an object using a depth-first method in which repetitive listening of unnecessary index occurs [4]. When backtracking occurs, query processing time increases, thereby decreasing users satisfaction as well as increasing mobile devices battery consumption. Recently, studies on spatial query processing in the road network environment over a wireless broadcasting environment have been proposed [5]. Index for spatial queries in wireless broadcast environment (ISW), proposed in Ref. [5], is a technique that supports snapshot spatial query processing effectively based on the road network over a wireless broadcasting environment; it can support range query, knn query, and reverse nearest neighbor (RNN) query processing. However, since a node constructed a tree using a method of MBR construction, the size of MBR increased as the number of nodes increased, thereby increasing the query processing time. Therefore, in this work, an efficient spatial query processing algorithm is proposed, taking the road network environment over wireless broadcasting into consideration. include a consideration of spatial query processing, it cannot be applied to the LBS. More recently, a technique supporting spatial query processing using the BGI technique [] was proposed. The BGI was proposed to support continuous query processing based on moving objects. The BGI arranges objects in a cell unit using the grid-based HC [30] and sends dirty grid information based on the bitmap, which represents the movement or lack of movement of an object in a cell. However, in cases where objects move frequently, its efficiency is reduced because objects included in all cells are moved. To overcome this weakness, an hierarchical bitmapbased spatial index (HBI) was proposed [3]. The HBI was proposed to divide a map according to the HC order to put a bit within the divided grid according to the presence of objects and to reduce the index size through the bitmap connected to the entire bits, thereby enabling selective tuning. 4 Spatial query processing (Snapshot) In the present work, we propose BLI and knn query processing using the BLI. This section explains an example of NN query processing. Prior to explaining Figs. 5 and 6, the terms used in this section are defined. N i refers to each node, and O i refers to each object. d e (q, O i ) is a value of the distance between a query point and O i measured with the Euclidian distance. d N (q, O i ) is a value of the actual road distance between a query point and O i. A map is configured through a node s bitmap and the connection information between nodes. While listening to objects bitmaps within a configured map sequentially, an object is created, as shown in Fig. 5, and the distance between a query point and an object is 3. Wireless broadcast environments In Ref. [9], (,m) indexing and distributed index over a wireless environment was proposed. The authors also proposed an index by which a battery can be used longer due to the index structure s efficiency. However, because the technique proposed in Ref. [9] did not Fig. 5 knn query processing (filtering) using Euclidian distance

5 3860 Fig. 6 knn query processing (validation) using road distance measured to process a NN query. First, a query point (q) is identified and the distance between O and q is measured followed by measuring the distance of O to set up a search area for NN. Once this process is repeated, d e (q, O 3 ) for NN will become the first filter for the search area. Algorithm : knn query processing (filtering) using Euclidian distance in road network Input: road map, object s bitmap (b o ) Output: k numbers of objects that are closest to a query point (q) from the straight line in road map result Oi : A set of actual candidate objects computer the query point q; begin the retrieve b o ; d e (q, O i )= ; d N (q, O i )= ; result =O i ; 3 while k<=result do 4 for each object O i covered by MBC do 5 if (MBC with O i ) then 6 result = result {O i }; result ={O i, O j,, O x }; 9 if d e (q, O i )<d e (q, O j ) 0 else if d e (q, O i )<d e (q, O x ) return result ; Figure 6 shows an expansion after measuring the actual road distance of q and objects obtained through the first filtering. As shown in Fig. 6, all the grids in the search area included in d N (q, O 3 ) should be checked. The checked results show that the search objects included in d N (q, O 3 ) are O 3, O 4, and O 5. The measurement results for the distance between a query point and the road of a search object show that the distance between a query point and O 5 is shorter than the distance between a query point and O 3. That is to say, objects for NN are O 4 and O 5. If the J. Cent. South Univ. (04) : Euclidian distance is used to reduce the search area of objects to search objects, O 5 is excluded from the search area so that a client cannot obtain a correct query result. But, through two rounds of BLI validation, a client can return a correct query result. Algorithm 3: knn query processing (validation) using road distance in road network Input: road map, objects obtained through the first filtering, object s bitmap (b o ) Output: k numbers of objects which are closest to a query point (q) over an actual road in the road map result O i : objects obtained through the first filtering computer the query point q; begin the retrieve b o ; d e (q, O i )= ; d N (q, O i )= ; result=null; 3 while k<=result do 4 for each object O i covered by MBC do 5 if (MBC with O i ) then 6 result = result {O i } result ={O i, O j,, O x } 9 0 if d e (q, O i ) < d e (q, O j ) else if d e (q, O i )< d e (q, O x ) return result ; (Once straight line distance between a query point and an object is sorted in order, objects up to k are stored in the result and returned). 5 Cost model In this section, we will compare HCI, BGI, and BLI. First, we explain how their indexes are configured mathematically. BGI (B) divides a map through δ, and each divided cell is called cell (i,j) cardinality data. The number of divided cells through δ is called δ n. The coordinate information on objects obtained through cell (i,j) cardinality data uses 4 bytes in the x axis and 4 bytes in the y axis. (If detailed coordinates are measured using the GPS, the data size in the x and y axes may increase.) The data size in the coordinates is times coordinate data (a) and O n refers to the total number of objects. HCI (H) can be expressed in the following equation: ( N n H m 4 O ) () B cell a ) (a ) (3) ( ( i, j) n On BLI (L) is divided into 4 N according to the HC order, but depending on the distribution level of the objects, the HC order can increase, which is represented as 4 N+α. HCI and BGI identify an object s location using a grid and thus require additional coordinate information. However, the proposed technique, BLI, does not require coordinate information because it identifies an object s location

6 J. Cent. South Univ. (04) : through a grid (intersection). N L= 4 (4) HCI, BGI and BLI are index structures that use (,m) indexing, and the proposed technique is compared using the results obtained through the mathematical analysis described in Ref. [9]. In Ref. [9], access latency was derived mathematically from the values obtained by adding probe wait to bcast wait. Index (I) refers to an index s size and Data ( refers to the information on an object s size. m is the number of index insertions during one cycle of broadcasting, and the coarseness of the (index) attribute is denoted as C. The above-mentioned access latency equation is applied to each proposed technique as follows: H B L A N (( m ) ((4 On ) (a On )) ( ) m (5) (( m ) (cell( i, j) a n ) (a On )( ) m (6) A A (( m ) 4 N ) ( ) m (7) Tuning time is (Index)+k(+C. The following shows that the tuning time is applied to each technique: N H T ( 4 On ) (aon ) k( (8) BT ( cell( i, j) a n ) (aon ) k( (9) Table Experimental data set value Parameter Set value Coordinate value/byte 4 Packet head size/byte Data size/byte 8 Number of objects (basic 0000) Number of nodes 5000 HC order, N 8 k 0 The resulting values of access latency (in Fig. 7(a)) show that the BLI improves performance by about 34.7% and.3% more on average than the HCI and BGI, respectively. The resulting values of tuning time in Fig. 7(b) show that the BLI improves performance by about 48.4% and 35.7% more on average than the HCI and BGI, respectively. Figure 8 shows the result when the number of objects k, the search target, changes to 3, 5, 0, and 0. Figure 8(a) shows the access time for each technique according to a change in k. As k increases, the number of objects increases, thereby increasing the search area, resulting in an increasing slope overall. The resulting values in Fig. 8(a) show that the BLI improves L T 4 N k( (0) 6 Experimental results We conducted an experiment on knn query using the BLI. In the experiments, the C++ programming language was used to actualize the algorithms on a.9 GHz CPU with 4 GB of main memory. We assumed the basic parameter setting values shown in Table to evaluate the performance. We compared the existing HCI and BGI with the proposed BLI. In the experiment, we assumed that information was obtained through a wireless broadcasting channel in two-dimensional space. Since access time and tuning time can differ depending on the bandwidth and transmission speed, the result value (the y axis) of the graph was represented as the data in this experiment. The connection information between nodes was removed from the performance evaluation since the connection information between nodes is all in the same condition. Fig. 7 0NN queries with different number of objects: (a) Access latency; (b) Tuning time

7 386 J. Cent. South Univ. (04) : broadcasting environment. The BLI can identify the locations and distances of nodes and objects through nodes bitmaps, objects bitmaps and the connection information between nodes. Then, it measures the distance between a query point and an object using the Euclidian distance to perform filtering of the search area. A client can reduce the search area through the first filtering and then check errors regarding an object s result by measuring the actual road distance between a query point and the object through the second validation, thereby obtaining the final value. The BLI can configure a roadmap using less data and support effective query processing. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF- 03RAA004593, 03RAAA050348). References Fig. 8 knn queries in relation to changes in k: (a) Access latency; (b) Tuning time performance by about 64.7% and 7.3% more on average than the HCI and BGI, respectively. Figure 8(b) shows that the BLI improves performance by about 44.3% and 5.% more on average than the HCI and BGI, respectively. In the case of tuning time, as k increases, the graph of BLI increases rapidly, as the BLI has a larger search area than the other techniques, thereby increasing the amount of data for objects requiring listening. 7 Conclusions Existing road networks are based on the on-demand method, thereby receiving surrounding information from adjacent nodes to process queries or transmitting the query result in a server to clients. The main disadvantage of the existing on-demand methods while receiving information from adjacent nodes is the rapid increase in the number of messages for queries, as a client must check not only adjacent nodes, but also all the nodes included in a search area if the search area increases. In addition, in a on-demand environment, a server s workload increases as the query request increases when a server sends a client information. To solve this problem, we proposed the BLI and applied it in a wireless [] ZHENG J, ZHU M, PAPADIAS D. Location-based spatial queries [C]// Proceeding of Special Interest Group on Management of Data. San Diego, California, USA, 003: [] BELLAVISTA P, KUPPER A, HELAL S. Location-based services: Back to the future [J]. IEEE Pervasive Computing, 008, 7(): [3] RAO B, MINAKAKIS L. Evolution of mobile location-based services [J]. Communications of the ACM, 003, 46(): [4] PAPADIAS D, ZHANG J, MAMOULIS N, TAO Y. Query processing in spatial network databases [C]// Proceeding of Very Large Data Bases. Berlin, Germany, 003: [5] SAMET H, SANKARANARAYANAN J, ALBORZI H. Scalable network distance browsing in spatial databases [C]// Proceeding of Special Interest Group on Management of Data. Vancouver, BC, Canada, 008: [6] SANKARANARAYANAN J, ALBORZI H, SAMET H. Efficient query processing on spatial networks [C]// Proceeding of Geographic Information Systems. Bremen, Germany, 005: [7] IMIELINSKI T, VISWANATHAN S, BADRINATH B R. Energy efficient indexing on air [C]// Proceeding of Management of Data. Minnesota, USA, 994: [8] PARK K, CHOO H. Energy-efficient data dissemination schemes for nearest neighbor query processing [J]. IEEE Transactions on Computers, 007, 56(6): [9] CHEN M S, WU K L, YU P S. Optimizing index allocation for sequential data broadcasting in wireless mobile computing [J]. IEEE Transactions on Knowledge and Data Engineering, 003, 5(): [0] ZHENG B, XU J, LEE W C, LEE L. Grid-partition index: A hybrid method for nearest-neighbor queries in wireless location-based services [J]. Very Large Data Bases Journal, 006, 5(): 39. [] LIN L F, CHEN C C, LEE C. Benefit-oriented data retrieval in data broadcast environments [J]. Wireless Networks, 00, 6(): 5. [] MOURATIDIS K, BAKIRAS S, PAPADIAS D. Continuous monitoring of spatial queries in wireless broadcast environments [J]. IEEE Transactions on Mobile Computing, 009, 8(0): [3] SONG D, PARK K. A Hierarchical bitmap-based spatial index for

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