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Efficient Processing of Top-k Spatial Keyword Queries

João B. Rocha-Junior, Orestis Gkorgkas, Simon Jonassen, and Kjetil Nørvåg

1SSTD 2011 - Minneapolis, Minnesota, USA

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Outline

• Top-k spatial keyword queries• Current approaches• Spatial inverted index • Single-keyword queries• Multiple-keyword queries• Experimental evaluation• Conclusion

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Motivation

• More and more documents in the Internet are being associated with a spatial location– Ex: tweets, images (Flickr), Wikipedia sites,

OpenStreetMap objects,…

• Most of these geotagged objects are associated with a text (description)

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Top-k spatial keyword queries

• Query – Spatial location– Query keywords

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Italianfood

• Returns the k best spatio-textual objects ranked in terms of both – Spatial distance to the

query location– Textual relevance to the

query keywords

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Another example…

• Query – Spatial location– Query keywords

• Returns the k best spatio-textual objects ranked in terms of both – Spatial distance to the

query location– Textual relevance to the

query keywords

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q

objects query location

distance

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Ranking objects

• Score

• The spatial proximity (δ) is the normalized Euclidean distance between p and q

• The textual relevance (θ) is the cosine similarity between the description of p and the query keywords

• The query preference parameter (α) defines the importance of one measure over the other

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€

τ ( p,q) = α ∗δ ( p,q) + (1 −α )∗θ ( p,q)

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Current approaches

• Employ a modified R-tree [1,2]– Each node keeps an abstract document

representing all documents in the node sub-tree• Abstract document– Pairs (term, weight), one pair per term– The weight permits computing an upper-bound

score for the objects in the node sub-tree

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[1] Cong, G., Jensen, C.S., Wu, D.: “Efficient retrieval of the top-k most relevant spatial web objects”, VLDB, 2009. [2] Li, Z., Lee, K.C., Zheng, B., Lee, W., Lee, D., Wang, X.: “IR-tree: an efficient index for geographic document search”, TKDE, 2010.

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Example

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e3

e2

root:

bar:2pop:2pub:1rock:1samba:1

e1: e2: e3:

bar:2pub:2samba:1

pop:1pub:1samba:1

e1

qq

e1 e2 e3

p5 p7p1 p2 p3 p4 p6

bar:1pop:2pub:1rock:1

e1

e1: p1 p2 p3

For simplicity, we assume that the impact of a term is defined by the frequency

rock:1pub:1

pub:2 pub:1

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Current approaches

• There are several variations– Incorporating document similarity– Clustering the nodes

• Main problems– Frequent and infrequent terms are stored in the

same way (have the same cost)– Accesses several nodes due to text dimensionality– Complex management of inverted files and/or

vectors, one per node

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Spatial inverted index (S2I)

• Similarly to an inverted index, S2I maps terms to objects that contain the term– The most frequent terms are stored in aggregated

R-trees (aR-trees)– The less frequent terms are stored in blocks in a file

• The aR-tree permits accessing the objects in decreasing order of term relevance

• The blocks permits storing the less frequent terms efficiently

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Distribution of terms

• The distribution of terms is very skewed• Few hundred terms take up 50% of the text

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Terms

Freq

uenc

y

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Example

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Aggregated R-tree (max) for frequent terms (e.g., pub)

• Only relevant objects are evaluated

• The objects are accessed in decreasing order of score

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e1

e2

e0e0:

e1: e2:

e1(1) e2(2)

p1(1) p2(1) p5(2) p6(2) p7(1)

, max=1

, max=2

TermimpactTerm

impact

MaxvalueMaxvalue

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Single-keyword queries

• Only a single block or tree is accessed• Block– All the objects are read and the k best are reported

• Tree– The nodes are accessed in decreasing order of score– The algorithm terminates when the score of the k-th

object is higher than the score of any unvisited node

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Example, processing top-1

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e1

e2

e0, max=1

, max=2

e0:

e2:

e1(1) e2(2)

p1(1) p2(1) p5(2) p6(2) p7(1)

Max-heap: <e1>

Minimum distance

Top-1

e1:

Max-heap: <e2, e1>Max-heap: <p5, p6, e1, p7>

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Multiple-keyword queries

• Requires aggregating the partial scores of the objects for each term t of the query keywords

• Similar to Fagin’s algorithm (NRA)– Different bounds

• Score:

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τ( p,q) = τ tt∈q.d∑ ( p ,q)

Partial scorePartial score

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Multiple-keyword algorithm

• For each term t in q, access the objects p in S2I in decreasing of partial score– The objects are retrieved from a tree or block

• Update the lower bound score of p– Sum of the partial scores know plus the lowest

possible partial score (using the spatial distance)• Update the upper bound score of the visited

objects• Return the objects whose lower bond score

cannot be overcame by the remaining objects

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Experimental evaluation

• We compare our approach (S2I) with the DIR-tree proposed by Cong et al. [1]

• Both approaches are implemented in Java• Measures: response time, I/O, update time,

and index size• Size of tree nodes and blocks: 4KB

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[1] Cong, G., Jensen C. S., Wu, D. “Efficient retrieval of the top-k most relevant spatial web objects”, VLDB, 2009.

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Datasets

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DatasetsTotal no. of objects

Avg. no. of unique terms per object

Total no. of terms

Twitter1 1M 11.94 12.5M

Twitter2 2M 12.00 25M

Twitter3 3M 12.26 38.6M

Twitter4 4M 12.27 51.6M

Data1 0.1M 131.70 32.6M

Wikipedia 0.4M 163.65 169.4M

Flickr 1.4M 14.49 25.4M

OpenStreetMap 3M 8.76 31.5M

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Variables studied

• Number of results– 10, 20, 30, 40, 50

• Number of query keywords– 1, 2, 3, 4, and 5

• Query preference rate (α)– 0.1, 0.3, 0.5, 0.7, 0.9

• Scalability (twitter dataset)– 1M, 2M, 3M, 4M

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Number of results (k)

• The response time of S2I is one order of magnitude better due to less disk accesses– DIR-tree reads several nodes before finding the top-k

due to text dimensionality

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Number of query keywords

• One order of magnitude better in I/O and response time

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Insertion time and index size

• S2I does not require updating inverted files (and vectors), and computing document similarity

• S2I requires more space

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Conclusions

• Top-k spatial keyword queries are intuitive and have several applications

• We propose a new index– Terms with different frequency are stored differently

• We propose algorithms to single- and multiple- keyword queries

• The efficiency of our approach is verified through experiments on synthetic and real datasets

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More information…João B. Rocha-Junior

joao@idi.ntnu.nohttp://www.idi.ntnu.no/~joao

Thanks!

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Scalability

• S2I improvement over DIR-tree increases with cardinality of the datasets

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Different datasets

• The advantage of S2I over DIR-tree is higher for datasets with few terms per documents

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Terms removal

• Terms with length=1• Terms that have no letter character– ! Character.isLetter(token.charAt(i))

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