NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query

Guihai Chen; Xiaofeng Gao; Xiuqi Huang; Yuanning Gao

REVIEW 4 major objections 6 minor 22 references

NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read NETR-Tree claims that a two-layer index combining social embeddings and a time-aware R-tree returns exact top-k places while visiting only a fraction of the data.

desk verdict The NETR-tree paper has a reasonable new problem and index design, but its exactness proof is false for the time and social score components, so the current version should not be accepted. read the letter →

arxiv 1908.09520 v2 pith:SERJUTM7 submitted 2019-08-26 cs.SI cs.IR

Xiuqi Huang , Yuanning Gao , Xiaofeng Gao , Guihai Chen This is my paper · ORCID

classification cs.SIcs.IR

keywords location-basedsocialnetworksspatialkeywordquerytop-knetworkembeddingtime-awareretrievalR-treeindexinfluence

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper formulates the Social-based Time-aware Spatial Keyword Query (STSKQ): find the top-k places for a user where the ranking combines geographic distance, textual relevance, how suitable the place is at the query time, and how much the user's social neighbors have visited it. It then proposes NETR-Tree, a two-layer index pairing a network-embedding user layer with a Time-aware R-tree location layer, plus a best-first search algorithm that prunes tree nodes by an upper-bound score. The paper's central claim is that the bound is valid—every internal node scores at least as high as any object inside it—so the search returns the exact top-k result without scanning all objects. Experiments on two real-world location-based social network datasets report lower query time and fewer node accesses than three baselines.

What carries the argument

The load-bearing object is the NETR-Tree itself, specifically its Time-aware R-tree (TR-tree) layer. Each non-leaf node stores a minimum bounding rectangle, a TF-IDF keyword summary, the maximum per-hour check-in probability among its descendants, and category entropy (how mixed the venue categories are inside the rectangle). The user layer stores network-embedding vectors and per-user check-in counts per node in user-inverted blocks. The argument runs through the composite score of Eq. (9): if every component of a node's score is at least the corresponding component of any descendant object, then the weighted sum is also an upper bound, and Algorithm 2 can safely prune by that bound.

What would settle it

On either real dataset used in the paper, compute Eq. (5) for every internal node and every descendant object and list any pair with $F_s(u,N) < F_s(u,o)$; if one exists, Theorem 1's premise fails and Algorithm 2 can return a wrong top-k. As an end-to-end check, run Algorithm 2 and a brute-force scan over all objects on the same queries and compare the result sets.

Watch

Extended reading notes

Core claim

The paper's central claim is that STSKQ—returning the $k$ places best matching a user's location, keywords, query time, and social circle—can be solved exactly by a best-first traversal of a two-layer hybrid index. The NETR-Tree combines a user layer, where each user has an embedding vector and per-node check-in counts, with a location layer, a Time-aware R-tree whose nodes store an MBR, a TF-IDF keyword summary, a maximum check-in time distribution, and category entropy. The score of any node is $F(u,q,\eta)=\alpha F_g(q,\eta)+\beta F_k(q,\eta)+\gamma F_s(u,\eta)+(1-\alpha-\beta-\gamma)F_t(\eta,q.t)$. Theorem 1 asserts that for any user $u$, query $q$, internal node $N$, and descendant object $o$, $F(u,q,N)\ge F(u,q,o)$, which makes the heap-based search exact: the first $k$ objects popped are the true top-k, and subtrees whose node score cannot beat the current k-th best are pruned.

Load-bearing premise

The argument collapses if an internal node can score lower than one of its own descendant objects on any of the four components, especially the social score, whose normalization compares a node against its siblings but an object against its siblings; if that inequality fails, best-first pruning can throw away the subtree containing the true answer.

Editorial extensions

If this is right

Under Theorem 1, Algorithm 2 can stop as soon as $k$ objects are popped from the heap, so the cost of a query is determined by how many nodes have upper-bound scores above the k-th best object, not by the total number of objects.
The same index answers queries under different balances of location, keyword, time, and social influence by reweighting $\alpha$, $\beta$, and $\gamma$, since the dominance argument applies component-wise before the weighted sum.
Any node whose check-in probability is zero at the query time can be discarded immediately, which lets the search skip entire subtrees of venues that are closed.
On a dataset with richer social connections, the social score contributes more pruning power, which is consistent with the paper's reported performance gap between its two datasets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct way to stress-test the exactness claim is to compute Eq. (5) for every internal-node/descendant pair on the same datasets and look for a pair with $F_s(u,N) < F_s(u,o)$; finding one would localize the exact fix needed in the pruning bound.
The two-layer design transfers to other graph-regularized top-k searches, such as trust-aware or expertise-aware recommendation, by replacing the check-in counts with any per-node user activity signal while keeping the same upper-bound structure.
The fixed weight setting in the experiments leaves open a per-user learned weighting of the four scores; if the monotonicity bound holds component-wise, any learned weights within the same convex family preserve exactness.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Rough take: the paper's core exactness guarantee is unsupported. The problem formulation and index combination are sensible, but Theorem 1 is false as stated, and the experiments never test whether the returned top-k sets are actually correct. I would desk-reject this version, though a corrected resubmission could be worth a look.

What's actually new: STSKQ as a query type, and NETR-tree as a single index that puts a time-aware R-tree together with LINE network embeddings and skyline-based neighbor selection. That combination is not in the cited prior work, so it's a legitimate, modest extension rather than a copy. The construction-cost comparison against IR-tree, Routing R-tree, and SKB-Inv is a reasonable empirical study of a practical index, and the paper is clearly written.

The problem is Theorem 1. The proof asserts component-wise monotonicity: every internal node scores at least as high as each descendant object. That holds for Fg (leaf entropy is 0, and MBR distance is conservative) and for Fk (max TF-IDF with a shared normalizer). It fails for Ft and Fs. For Ft, N.T(t) stores the max of descendants' per-slot probabilities, but the denominator in Eq. (3) is the max over that node's own distribution, so a descendant can have a higher ratio. Concretely, two children with time distributions (0.5, 0.5) and (0, 1) give the parent Ft = 0.5/1 = 0.5 at the first slot while the first child has Ft = 0.5/0.5 = 1. For Fs, Eq. (5) normalizes by the max among the node's brothers, while Eq. (4) normalizes by the max among the object's siblings; those brother sets differ, so the social ratio can drop as you go up the tree. The category-entropy part is not the issue; the failures of Ft and Fs are enough. Because Algorithm 2's best-first traversal prunes entries with score lower than the current k-th result, a subtree containing a true top-k object can be discarded.

The experiments measure only runtime and I/O; they never compare the returned top-k set against a brute-force baseline, so nothing in the evaluation repairs the proof gap. No code or data are released, and hyperparameters like ST-DBSCAN's Eps and MinPts are missing. Those are secondary to the proof flaw but would matter in a revision.

Who is this for: someone working on LBSN query processing might want the problem formulation and the general index idea as a starting point. But as submitted, the central guarantee is unsupported. I would desk-reject and invite a resubmission with a corrected monotone scoring scheme (e.g., consistent domination bounds instead of per-node normalization) and an exactness/quality comparison against brute force.

Referee Report

4 major / 6 minor

Summary. The paper formulates the Social-based Time-aware Spatial Keyword Query (STSKQ), which ranks spatial objects by a weighted combination of geo-spatial, keyword, visiting-time, and social-relationship scores. To process STSKQ, it proposes the NETR-tree, a two-layer index that combines user network embeddings with a Time-aware R-tree (TR-tree). The central correctness claim is Theorem 1, which asserts that the composite score of an internal TR-tree node is an upper bound on the score of every descendant object, thereby justifying a best-first traversal (Algorithm 2) that is claimed to return the exact top-k results. The experimental section compares NETR-tree with three baselines on Yelp and Weeplaces, reporting processing time and disk I/O.

Significance. If the exactness guarantee were valid, the paper would make a useful contribution by integrating social and temporal dimensions into spatial keyword query processing and by providing an index with an intuitive best-first pruning strategy. The problem formulation is timely, the index design is coherent, and the authors evaluate on two real-world datasets. However, the central theorem is demonstrably false as stated, and the experiments measure only efficiency, never the quality of the returned results. Because the load-bearing theoretical guarantee and the claimed effectiveness are both unsupported, the contribution cannot be accepted in its present form.

major comments (4)

[§5, Theorem 1 and Eq. (3)] The proof of Theorem 1 asserts that Ft(N,t) >= Ft(o,t) for an internal node N and descendant object o because N.T(t) is the maximum of descendant T-values. This is false because Eq. (3) normalizes by each node's own maximum over time intervals. For example, with two time slots and descendants o1.T=(0.5, 0.5) and o2.T=(0, 1), the parent has N.T(t1)=0.5 and max_tau N.T(tau)=1, so Ft(N,t1)=0.5, while Ft(o1,t1)=0.5/0.5=1. Thus the component score decreases with containment, and since the composite score in Eq. (9) is a weighted sum, F(u,q,N) >= F(u,q,o) can fail. Algorithm 2 therefore may prune a subtree containing true top-k answers, so the exactness guarantee is not established.
[§5, Theorem 1 and Eqs. (4)-(5)] The same component-wise monotonicity is assumed for the social score Fs, but Eq. (4) normalizes C(ui,o) by the maximum over o's brothers, while Eq. (5) normalizes C(ui,N) by the maximum over N's brothers. Since these brother sets differ, the ratio can decrease with containment even though C(ui,N) is maximal. Concretely, let N have C(ui,N)=5 and a brother M with C(ui,M)=100, so the parent-level denominator is 100; let descendant object o have C(ui,o)=5 and a sibling o' with C(ui,o')=10, so the object-level denominator is 10. Then Fs(u,o)=0.5 while Fs(u,N)=0.05, contradicting the asserted inequality. The proof's statement that 'user check-in number is maximal' does not imply monotonicity of the normalized score.
[§6, experimental evaluation] The experiments report only average processing time and disk I/O. They do not compare NETR-tree's output against a brute-force exact computation or any quality metric such as precision, recall, or NDCG. Consequently, the abstract's and conclusion's claims of 'effectiveness' are not demonstrated; the efficiency measurements could be for result sets that do not match the true top-k ranking, especially given the failure of Theorem 1.
[§6.2, Table 2] The score function in Eq. (9) contains user-set weights alpha, beta, gamma, and theta, and the index construction depends on LINE embedding dimension d and ST-DBSCAN parameters. Only gamma is swept (Fig. 6), on the same evaluation datasets; alpha, beta, theta, d, Eps, and MinPts are fixed without sensitivity analysis or a principled selection. Since the composite score is defined by the authors, the reported efficiency gains may be sensitive to these arbitrary choices, and the lack of a held-out evaluation of the scoring model weakens the empirical contribution.

minor comments (6)

[Title] The word 'Eifficient' in the title is a typo and should be 'Efficient'.
[Definition 4 and Fig. 1] The term 'cEntroy' is used instead of 'cEntropy'; the typo appears in the definition and in the overview figure.
[Eq. (4)] The summation in Eq. (4) is over 'u.Fds', but the text and Eq. (5) use 'u.Nrs'; this notation should be made consistent.
[§6.2] The values of the LINE embedding dimension d and the ST-DBSCAN parameters Eps and MinPts are not reported, which makes the experiments difficult to reproduce.
[Fig. 2] The axis labels in Fig. 2 are garbled in the submitted version; the figure should be regenerated with clear labels.
[§7] The conclusion claims 'extensive experiments using two real datasets verify the efficiency and effectiveness of NETR-tree,' but effectiveness is not measured anywhere in Section 6.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the STSKQ score is defined from explicit components with user-set weights, and the efficiency claims are tested against independent baselines; the unsound monotonicity step in Theorem 1 is a correctness issue, not a circular derivation.

full rationale

The paper's derivation chain is not circular. The composite score F in Eq. (9) is assembled from explicitly defined components: geo-spatial score Fg (Eq. 7), keyword similarity Fk (Eq. 8), social effect Fs (Eq. 5), and visiting time score Ft (Eq. 3), with user-supplied weights alpha, beta, gamma, and theta. None of these parameters is fitted to reproduce a known top-k result, and the top-k answer is not encoded in the score definition. Theorem 1 is presented as a proof of a monotonicity property of this score over tree containment; even if the proof is unsound because Ft and Fs normalize by node-specific denominators (so the claimed inequalities Ft(N,t) >= Ft(o,t) and Fs(u,N) >= Fs(u,o) do not follow from maximal raw check-in values), that is a mathematical gap or correctness risk, not a reduction of the claim to its inputs by construction. The experimental section evaluates processing time and I/O against independent baselines (IR-tree, Routing R-tree, SKB-Inv index), and the gamma sweep in Fig. 6 is internal parameter tuning rather than a manufactured comparison. The cited prior work by the same authors is not load-bearing for the central claim, and no uniqueness theorem or fitted input is invoked as a substitute for derivation. Accordingly, there is no significant circularity, so the score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The exactness claim rests on an unproven monotonicity property of the score, plus domain assumptions about check-in distributions and embedding-based social influence. The weighting parameters are chosen by hand. No new physical or conceptual entities are introduced beyond the NETR-Tree index itself.

free parameters (6)

alpha, beta in Eq. (9) = 0.25, 0.25
Weights for geo-spatial and keyword scores, set by hand in Sec. 6.2 rather than derived.
theta in Eq. (7) = 0.5
Weight between category entropy and location proximity, set by hand in Sec. 6.2.
gamma in Eq. (9) = 0.3 default, swept 0.1 to 0.5
Weight for social effect; default 0.3 is chosen after observing best Yelp processing time at 0.3 in Fig. 6.
delta_max in Eq. (6) = 12 km default, swept 4 to 20 km
Maximal search radius used for location proximity normalization; an experimental parameter.
LINE embedding dimension d = not reported
Dimension of user embedding vectors V in Sec. 4.2; never specified, directly affects the social score.
ST-DBSCAN parameters (Eps, MinPts) = not reported
Parameters for the spatio-temporal clustering used in neighbor selection, Sec. 4.2.

assumptions (5)

ad hoc to paper Composite score monotonicity: F(u,q,N) >= F(u,q,o) for every internal node N and descendant object o.
Stated as Theorem 1 and used to justify best-first pruning. The proof's Fs inequality is invalid, so this is a load-bearing unsupported assumption.
ad hoc to paper Category entropy of a TR-tree node is not less than that of its descendants.
Used in the proof of Theorem 1 for Fg in Eq. (7). Entropy of a merged distribution is not guaranteed to be at least the entropy of a subset distribution, so the inequality can fail.
domain assumption Check-in frequency distribution over hourly slots is a valid proxy for visiting suitability.
Used to define Ft in Eq. (3), following the periodicity observation in [7]. This is a modeling choice, not a proven fact.
domain assumption LINE embedding cosine similarity between users captures social influence on venue choice.
Used to define Fs in Eq. (5). The paper does not validate that embedding similarity correlates with check-in influence beyond the check-in counts already used.
ad hoc to paper Skyline non-dominated users plus friends are the only neighbors affecting the target user.
Neighbor selection in Sec. 4.2 restricts the social influence set to friends and skyline users; this cutoff is arbitrary and unvalidated.

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Cite this review

Pith. "Pith review of NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query." pith.science (2026). https://pith.science/paper/SERJUTM7

@misc{pith2026190809520,
  author       = {Pith},
  title        = {Pith review of: NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SERJUTM7}},
  note         = {Machine review of arXiv:1908.09520}
}

read the original abstract

The development of global positioning system stimulates the popularity of location-based social network (LBSN) services. With a large volume of data containing locations, texts, check-in information, and social relationships, spatial keyword queries in LBSNs have become increasingly complex. In this paper, we identify and solve the Social-based Time-aware Spatial Keyword Query (STSKQ) that returns the top-k objects by considering geo-spatial score, keywords similarity, visiting time score, and social relationship effect. To tackle STSKQ, we propose a two-layer hybrid index structure called Network Embedding Time-aware R-tree (NETR-Tree). In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network. In the location layer, we build a Time-aware R-tree (TR-tree) considered spatial objects' spatiotemporal check-in information, and present a corresponding query processing algorithm. Finally, extensive experiments on two different real-life LBSNs demonstrate the effectiveness and efficiency of our methods, compared with existing state-of-the-art methods.

Figures

Figures reproduced from arXiv: 1908.09520 by the authors.

**Figure 1.** Overview of NETR-tree network structure. Supplied by a novel neighbor selection method, for a user u, the social effect of u’s neighbors can be calculated by the similarity between their embedding vectors and neighbors’ historical check-ins. In TR-tree part, each internal node records spatio-textual information together with check-in time distribution. According to the query tuple, TR-tree prunes out spatio-textuall… view at source ↗

**Figure 2.** (a)(b). On one hand, Routing R-tree maintains an R-tree for every timeinterval while an object can exist in many different R-trees simultaneously. On the other hand, SKB-Inv index maintains an inverted list for every time interval, keyword, and user’s check-in record. Both of these methods lead to extremely high redundancy. In contrast, in [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 4.** Effect of |q.W| IR-tree retrieves large numbers of the false positive objects in its candidate, and the additive operation to filter those objects leads to high time cost. Effect of |q.W|: Then, we study the impact of the number of query keywords, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Effect of search radius 0.1 0.2 0.3 0.4 0.5 (a) Varying on Yelp 0 10 20 30 40 50 Response Time(sec) NETR-tree Time 0.1 0.2 0.3 0.4 0.5 (b) Varying on Yelp 0 5000 10000 15000 20000 25000 30000 # Node Accesses NETR-tree I/O 0.1 0.2 0.3 0.4 0.5 (c) Varying on Weeplaces 0 …

**Figure 6.** Figure 6: Effect of γ Effect of search radius (km): In this experiment, we evaluate the influence of different search radii. As depicted in [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages

[1]

Data Knowledge Engineering (DKE) 60(1), 208–221 (2007)

Birant, D., Kut, A.: St-dbscan: An algorithm for clustering spatial–temporal data. Data Knowledge Engineering (DKE) 60(1), 208–221 (2007)

work page 2007
[2]

ACM Computing Surveys (CSUR) 47(2), 15 (2015)

Campos, R., Dias, G., Jorge, A.M., Jatowt, A.: Survey of temporal information retrieval and related applications. ACM Computing Surveys (CSUR) 47(2), 15 (2015)

work page 2015
[3]

In: International Conference on Conceptual Mod- eling (ER)

Cao, X., Chen, L., Cong, G., Jensen, C.S., Qu, Q., Skovsgaard, A., Wu, D., Yiu, M.L.: Spatial keyword querying. In: International Conference on Conceptual Mod- eling (ER). pp. 16–29. Springer (2012)

work page 2012
[4]

IEEE Transactions on Knowledge and Data Engineering (TKDE) 29(11), 2601–2614 (2017)

Chen, G., Zhao, J., Gao, Y., Chen, L., Chen, R.: Time-aware boolean spatial keyword queries. IEEE Transactions on Knowledge and Data Engineering (TKDE) 29(11), 2601–2614 (2017)

work page 2017
[5]

In: IEEE International Conference on Data Engineering (ICDE)

Chen, L., Cong, G., Cao, X., Tan, K.L.: Temporal spatial-keyword top-k pub- lish/subscribe. In: IEEE International Conference on Data Engineering (ICDE). pp. 255–266 (2015)

work page 2015
[6]

The Proceedings of the Very Large Database Endowment Endowment (PVLDB) 6(3), 217–228 (2013) NETR-Tree for Social-Based Time-Aware Spatial Keyword Query 15

Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. The Proceedings of the Very Large Database Endowment Endowment (PVLDB) 6(3), 217–228 (2013) NETR-Tree for Social-Based Time-Aware Spatial Keyword Query 15

work page 2013
[7]

In: ACM Knowledge discovery and data mining (KDD)

Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: ACM Knowledge discovery and data mining (KDD). pp. 1082–1090 (2011)

work page 2011
[8]

IEEE Transactions on Intelligent Transportation Sys- tems(ITS) 17(2), 469–480 (2016)

Gao, Y., Zhao, J., Zheng, B., Chen, G.: Eﬃcient collective spatial keyword query processing on road networks. IEEE Transactions on Intelligent Transportation Sys- tems(ITS) 17(2), 469–480 (2016)

work page 2016

Show all 22 references

[9]

In: International Conference on Service- Oriented Computing (ICSOC)

Khani, M., Wang, Y., Orgun, M.A., Zhu, F.: Context-aware trustworthy service evaluation in social internet of things. In: International Conference on Service- Oriented Computing (ICSOC). pp. 129–145. Springer (2018)

2018
[10]

The International Journal on Very Large Data Bases(The VLDB Journal) 23(1), 129–145 (2014)

Lee, J., Hwang, S.W.: Toward eﬃcient multidimensional subspace skyline compu- tation. The International Journal on Very Large Data Bases(The VLDB Journal) 23(1), 129–145 (2014)

2014
[11]

IEEE Transactions on Knowledge and Data Engineering (ICDE) 23(4), 585–599 (2011)

Li, Z., Lee, K.C., Zheng, B., Lee, W.C., Lee, D., Wang, X.: Ir-tree: An eﬃcient index for geographic document search. IEEE Transactions on Knowledge and Data Engineering (ICDE) 23(4), 585–599 (2011)

2011
[12]

IEEE Transactions on Knowledge and Data Engineering (TKDE) 27(5), 1288–1300 (2015)

Liu, X., Chen, L., Wan, C.: Linq: A framework for location-aware indexing and query processing. IEEE Transactions on Knowledge and Data Engineering (TKDE) 27(5), 1288–1300 (2015)

2015
[13]

Information Sciences (Inf

Liu, X., Wan, C., Xiong, N.N., Liu, D., Liao, G., Deng, S.: What happened then and there: Top-k spatio-temporal keyword query. Information Sciences (Inf. Sci.) 453, 281–301 (2018)

2018
[14]

In: ACM International Conference on Con- ference on Information and Knowledge Management (CIKM)

Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood charac- teristics for location recommendation. In: ACM International Conference on Con- ference on Information and Knowledge Management (CIKM). pp. 739–748 (2014)

2014
[15]

In: International Conference of Learning Representation Workshop (ICLR Workshop)

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Eﬃcient estimation of word repre- sentations in vector space. In: International Conference of Learning Representation Workshop (ICLR Workshop). pp. 114–125 (2013)

2013
[16]

In: ACM Knowledge discovery and data mining (KDD)

Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social repre- sentations. In: ACM Knowledge discovery and data mining (KDD). pp. 701–710 (2014)

2014
[17]

The Computer Journal 61(11), 1620–1638 (2018)

Sohail, A., Cheema, M.A., Taniar, D.: Social-aware spatial top-k and skyline queries. The Computer Journal 61(11), 1620–1638 (2018)

2018
[18]

In: ACM International Conference on World Wide Web (WWW)

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale in- formation network embedding. In: ACM International Conference on World Wide Web (WWW). pp. 1067–1077 (2015)

2015
[19]

The International Journal on Very Large Data Bases(The VLDB Journal) 26(3), 301–326 (2017)

Wang, X., Zhang, W., Zhang, Y., Lin, X., Huang, Z.: Top-k spatial-keyword pub- lish/subscribe over sliding window. The International Journal on Very Large Data Bases(The VLDB Journal) 26(3), 301–326 (2017)

2017
[20]

In: IEEE International Conference on Data Mining (ICDM)

Wen, Y.T., Cho, K.J., Peng, W.C., Yeo, J., Hwang, S.W.: Kstr: Keyword-aware skyline travel route recommendation. In: IEEE International Conference on Data Mining (ICDM). pp. 449–458 (2015)

2015
[21]

The International Journal on Very Large Data Bases (The VLDB Journal) 21(6), 797–822 (2012)

Wu, D., Cong, G., Jensen, C.S.: A framework for eﬃcient spatial web object re- trieval. The International Journal on Very Large Data Bases (The VLDB Journal) 21(6), 797–822 (2012)

2012
[22]

The International Journal on Very Large Data Bases (The VLDB Journal) 27(2), 225–244 (2018)

Zhang, D., Li, Y., Cao, X., Shao, J., Shen, H.T.: Augmented keyword search on spatial entity databases. The International Journal on Very Large Data Bases (The VLDB Journal) 27(2), 225–244 (2018)

2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Data Knowledge Engineering (DKE) 60(1), 208–221 (2007)

Birant, D., Kut, A.: St-dbscan: An algorithm for clustering spatial–temporal data. Data Knowledge Engineering (DKE) 60(1), 208–221 (2007)

work page 2007

[2] [2]

ACM Computing Surveys (CSUR) 47(2), 15 (2015)

Campos, R., Dias, G., Jorge, A.M., Jatowt, A.: Survey of temporal information retrieval and related applications. ACM Computing Surveys (CSUR) 47(2), 15 (2015)

work page 2015

[3] [3]

In: International Conference on Conceptual Mod- eling (ER)

Cao, X., Chen, L., Cong, G., Jensen, C.S., Qu, Q., Skovsgaard, A., Wu, D., Yiu, M.L.: Spatial keyword querying. In: International Conference on Conceptual Mod- eling (ER). pp. 16–29. Springer (2012)

work page 2012

[4] [4]

IEEE Transactions on Knowledge and Data Engineering (TKDE) 29(11), 2601–2614 (2017)

Chen, G., Zhao, J., Gao, Y., Chen, L., Chen, R.: Time-aware boolean spatial keyword queries. IEEE Transactions on Knowledge and Data Engineering (TKDE) 29(11), 2601–2614 (2017)

work page 2017

[5] [5]

In: IEEE International Conference on Data Engineering (ICDE)

Chen, L., Cong, G., Cao, X., Tan, K.L.: Temporal spatial-keyword top-k pub- lish/subscribe. In: IEEE International Conference on Data Engineering (ICDE). pp. 255–266 (2015)

work page 2015

[6] [6]

The Proceedings of the Very Large Database Endowment Endowment (PVLDB) 6(3), 217–228 (2013) NETR-Tree for Social-Based Time-Aware Spatial Keyword Query 15

Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. The Proceedings of the Very Large Database Endowment Endowment (PVLDB) 6(3), 217–228 (2013) NETR-Tree for Social-Based Time-Aware Spatial Keyword Query 15

work page 2013

[7] [7]

In: ACM Knowledge discovery and data mining (KDD)

Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: ACM Knowledge discovery and data mining (KDD). pp. 1082–1090 (2011)

work page 2011

[8] [8]

IEEE Transactions on Intelligent Transportation Sys- tems(ITS) 17(2), 469–480 (2016)

Gao, Y., Zhao, J., Zheng, B., Chen, G.: Eﬃcient collective spatial keyword query processing on road networks. IEEE Transactions on Intelligent Transportation Sys- tems(ITS) 17(2), 469–480 (2016)

work page 2016