Query Processing of Geosocial Data in Location-Based Social Networks
Abstract
:1. Introduction
- To study how query processing methods are applied to geosocial data by researchers and practitioners, categorising them according to the kinds of geosocial queries, the kind of method(s) used to retrieve the result of the query, the kind of access method, and the opportunity to provide an approximate solution;
- To summarise the metrics and datasets used to evaluate geosocial queries in location-based social networks;
- To point out the primary research challenges in this field that emerged from analysing the literature.
2. Preliminary Concepts
2.1. Definitions of LBSN or Geosocial Networks
2.2. The Process of Querying Geosocial Data
- Best-first search algorithm: it allows to explore paths to search in the geosocial graphs by using an evaluation function to decide which among the various available nodes is the most promising to explore [13];
- Depth-first search algorithm: it allows to explore paths to search in the geosocial graphs by starting at a given node and exploring as far as possible along each branch before backtracking [14];
- Dijkstra search algorithm: it allows to find, for a given source node in the geosocial graph, the shortest path between that node and every other node [15];
- Branch and bound algorithm: it allows to explore branches of the geosocial graphs, which represent subsets of the solution set, by checking against upper and lower estimated bounds on the optimal solution and then enumerates only the candidate solutions of a branch that can produce a better solution [16];
- Measure and conquer algorithm: it allows to explore branches of the geosocial graphs, by using a (standard) measure of the size of the subsets of the solution set (e.g., number of vertices or edges of graphs, etc.) to lower bound the progress made by the algorithm at each branching step [17].
3. Research Methodology
3.1. Identifying the Review Focus
3.2. Specifying the Review Questions
- RQ 1: What kinds of geosocial queries are proposed in the literature? This question aims to identify the main categories of geosocial queries;
- RQ 2: What are the query processing methods applied to geosocial data? This question aims to identify the methodologies and query patterns and trends;
- RQ 3: How geosocial query processing methods are evaluated? This question aims to identify the metrics and datasets used to evaluate geosocial queries in LBSN;
- RQ 4: Which open challenges in geosocial querying have been envisaged? This question aims to identify the challenges and future research directions in the area of study.
3.3. Identifying Studies to Include in the Review
3.4. Data Extraction and Study Quality Appraisal
- Kind of geosocial query;
- Geosocial query processing method;
- Indexing method;
- Approximate solution (if available);
- Evaluation method(s);
- Evaluation metric(s);
- Evaluation dataset(s);
- Future/open challenges.
4. Results of the SLR and Quantitative Analysis
5. Findings and Discussion
5.1. RQ 1: What Kinds of Geosocial Queries Are Proposed in the Literature?
5.1.1. Geosocial Group Queries
- Distance: typical distance functions are Euclidean distance for items that are located in a small area; network distance, which is the length of the shortest path between the items on the road network of the search area; and Haversine formula, which is the distance between the items on the surface of a sphere [43].
- Range: the locations of the retrieved items (users/objects/PoIs) are within the query region.
- Coverage: the coverage of a set of query points is the minimum rectangle containing all query points.
- Travel cost, which is the expected cost of a direct travel from one item to the other.
- Friendship: in a geosocial network, friendship relations correspond to the edges between two nodes representing users.
- Interest/preference score: considers the interest(s)/preference(s) of a user or a group of users in spatial objects annotated by one or more keywords and can be computed by its/their check-ins on these spatial objects.
- Closeness: it restricts the users in a social group considering the proximity of candidate attendees to corresponding locations in the physical world, and sometimes even the ratings of assembly points as additional references [38].
- Acquaintance: it imposes a minimum degree on the familiarity of group members (which may include q); i.e., every user in the group should be familiar with at least k other users [52]. It is a measure of group cohesiveness. The value of k can be defined according to a minimum social distance that should be less than or equal to an acceptable social boundary.
5.1.2. Geosocial Keyword Queries
- Relevance: it is obtained from the number of fans and the relationship between these fans and the query user, where a fan is a user who exhibits positive behavior towards an object (e.g., check-in, like, share, etc.) [23];
- Relationship effect: it can be measured by the similarity of embedding vectors between users and their neighbors with all users’ check-in records [25].
5.1.3. Geosocial Top-k Queries
5.1.4. Geosocial Skyline Queries
- Social influence: it is applied to retrieve friends who have closer social ties and it is computed based on both the social connections and similarity of the check-in activities [50].
- Social similarity: it measures how socially close people are. Several methods for measuring this proximity have been proposed in the literature, and the most adopted are the Random Walks with Restart method and the Bookmark Coloring Algorithm, which considers all walks between two users [55].
5.1.5. Geosocial Nearest Neighbor Queries
5.1.6. Geosocial Moving Queries
5.1.7. Geosocial Fuzzy Queries
5.1.8. Frameworks Supporting Geosocial Query Processing
- J-CO framework [34] that provides a data model, an execution model, and a pool of operators (basic and spatial), which constitute the query language for querying heterogeneous collections of geo-referenced data and social network information.
- GeoSocial-GraphX platform [12] that incorporates several query primitives (social, spatial and activity) essential for LBSN queries.
- Socio-Spatial Network Algebra [77] that is composed of a set of seven operators that serve as the building blocks of a socio-spatial query language over a joined socio-spatial graph.
5.2. RQ 2: What Are the Query Processing Methods Applied to Geosocial Data by Selected Studies?
5.3. RQ 3: How Are Geosocial Query Processing Methods Evaluated?
5.3.1. Metrics
- Query response time, also named the query elapsed time or query processing time, which measures the time elapsed from the instant a query is issued to its result retrieval;
- Running time, also called the computation time, which is the length of time required to perform the query computational process;
- CPU time, which is the amount of time for which a central processing unit (CPU) is used for processing query instructions. According to what exactly the CPU is processing, this metric can be distinguished in client CPU time, which is the amount of time the CPU is busy executing client instructions, and the server CPU time, which is the amount of time the CPU is busy executing server instructions;
- Communication overhead, which is defined as the number of encrypted records sent as the result of an issued query [84];
- Correctness, which is the ratio between the number of the correct answers and the number of total queries;
- Accuracy, which is computed as the ratio between the cost functions of the result set obtained by the proposed query and the baseline solution [60];
- Index construction time, which can be defined as the time elapsed to construct the index structures [85];
- Approximation ratio, which is the usual way of measuring the performance of the query processing methods that provide approximate solutions and is computed as the ratio of the radius of approximate solution returned over that of the exact solution;
- I/O cost, which corresponds to the number of page/blocks accessed (I/O) to retrieve the data from the disk for each query;
- Pruning rate, which is computed as the ratio of the pruned PoIs to all the PoIs in the query range;
- Memory space, which is the total amount of memory used by the algorithm for query processing.
5.3.2. Evaluation Datasets
5.4. RQ 4: Which Open Challenges in Geosocial Querying Have Been Envisaged?
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Armenatzoglou and Papadias’ Geosocial Networking Topics | Search Keywords | Number of Published Articles Retrieved from WoS |
---|---|---|
Social and spatial data management | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “data management”) | 1 |
Query processing | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “quer*”) | 11 |
Link prediction | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “predict*”) | 7 |
Recommendations | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “recommend*”) | 71 |
Metrics | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “metric*”) | 2 |
Privacy | ((“geosocial networking” OR “geosocial network*” OR “location-based social network*”) AND “privacy”) | 33 |
Primitive | Description |
---|---|
Filter | Removes some vertices or edges from the graph that do not satisfy a selection condition. |
Partitioning | Compute a partition of the vertex set into n parts of size c. |
Scoring/Ranking | Ranks the vertices based on a scoring function to predict the values associated with each vertex. |
Sorting | Re-arrange the vertices on the graph according to one or more keys. |
Join | Compute the join between two vertex sets if a condition defined on their features is satisfied. |
Clustering | Partition the vertex set into a certain number of clusters so that vertices in the same cluster should be similar to each other, |
Pruning | Simplify a graph by reducing the number of edges while preserving the maximum path quality metric for any pair of vertices in the graph. |
Exclusion Criteria | |
---|---|
e1 | Duplication criterion:
|
e2 | Availability criterion:
|
e3 | Understandability criterion:
|
Inclusion Criteria | |
i1 | Relevance criterion:
|
i2 | Temporal criterion:
|
Quality Assessment Questions | Scores | |
---|---|---|
QA1 | Does the article describe a geosocial query processing method? | 1—yes, the geosocial query processing method is fully described. 0.5—partially, the geosocial query processing method is only summarised without describing in detail some steps. 0—no, the geosocial query processing method is only cited, without describing it. |
QA2 | Does the article describe the geosocial data representation schema? | 1—yes, the geosocial data representation schema is fully described. 0.5—partially, the geosocial data representation schema is only summarised without describing it in detail. 0—no, the geosocial data representation schema is not described. |
QA3 | Does the article provide an evaluation of the geosocial query processing method? | 1—yes, the geosocial query processing method is evaluated. 0—no, the geosocial query processing method is not evaluated. |
QA4 | Does the article state the open/future challenges? | 1—yes, the open/future challenges are clearly stated. 0—no, the open/future challenges are not stated. |
ID | Reference | Kind of Source | Year of Publication | Publisher | Citation Count |
---|---|---|---|---|---|
S1 | [34] | Conference | 2017 | Springer | 15 |
S2 | [2] | Conference | 2013 | ACM | 92 |
S3 | [35] | Conference | 2019 | Springer | 0 |
S4 | [36] | Journal | 2019 | Elsevier | 2 |
S5 | [18] | Conference | 2017 | IEEE | 8 |
S6 | [37] | Conference | 2012 | Springer | 50 |
S7 | [38] | Journal | 2020 | IEEE | 1 |
S8 | [39] | Journal | 2020 | IEEE | 0 |
S9 | [40] | Conference | 2013 | ACM | 132 |
S10 | [41] | Journal | 2018 | Springer | 1 |
S11 | [42] | Conference | 2020 | ACM | 1 |
S12 | [43] | Conference | 2020 | Springer | 2 |
S13 | [28] | Thesis | 2016 | repository.hkbu.edu.hk | 0 |
S14 | [44] | Journal | 2020 | IEEE | 2 |
S15 | [45] | Journal | 2016 | ACM | 3 |
S16 | [46] | Journal | 2020 | Elsevier | 1 |
S17 | [47] | Conference | 2019 | Springer | 1 |
S18 | [48] | Journal | 2020 | Taiwan Academic Network Management Committee | 0 |
S19 | [49] | Journal | 2017 | Springer | 3 |
S20 | [50] | Journal | 2016 | w.bncss.org | 3 |
S21 | [51] | Journal | 2016 | Elsevier | 11 |
S22 | [52] | Journal | 2017 | Springer | 52 |
S23 | [27] | Journal | 2015 | IEEE | 29 |
S24 | [53] | Conference | 2017 | Springer | 1 |
S25 | [54] | Journal | 2015 | Springer | 29 |
S26 | [55] | Conference | 2014 | Springer | 23 |
S27 | [24] | Conference | 2020 | Springer | 2 |
S28 | [23] | Journal | 2020 | mdpi.com | 2 |
S29 | [56] | Conference | 2019 | IEEE | 2 |
S30 | [57] | Conference | 2017 | Springer | 5 |
S31 | [58] | Conference | 2013 | Springer | 54 |
S32 | [59] | Conference | 2017 | Springer | 10 |
S33 | [60] | Journal | 2018 | IEEE | 3 |
S34 | [20] | Conference | 2015 | microsoft.com | 10 |
S35 | [61] | Journal | 2020 | Elsevier | 0 |
S36 | [62] | Conference | 2018 | Springer | 4 |
S37 | [63] | Journal | 2016 | mdpi | 5 |
S38 | [64] | Journal | 2019 | Elsevier | 0 |
S39 | [25] | arxiv | 2019 | arxiv.org | 0 |
S40 | [19] | Journal | 2020 | Elsevier | 2 |
S41 | [65] | Journal | 2018 | IEEE | 19 |
S42 | [22] | Conference | 2012 | ACM | 107 |
S43 | [66] | Conference | 2018 | Springer | 2 |
S44 | [67] | Journal | 2017 | Springer | 12 |
S45 | [68] | Conference | 2018 | search.ieice.org | 2 |
S46 | [69] | Thesis | 2015 | etda.libraries.psu.edu | 0 |
S47 | [70] | Journal | 2020 | IEEE | 0 |
S48 | [12] | Conference | 2016 | IEEE | 0 |
S49 | [26] | Conference | 2020 | IEEE | 5 |
S50 | [21] | Journal | 2020 | Elsevier | 1 |
S51 | [71] | Journal | 2018 | academic.oup.com | 13 |
S52 | [72] | Journal | 2015 | IEEE | 20 |
S53 | [73] | Journal | 2016 | ACM | 8 |
S54 | [74] | Journal | 2014 | Elsevier | 41 |
S55 | [75] | Journal | 2018 | Springer | 5 |
S56 | [76] | Conference | 2018 | IEEE | 8 |
S57 | [77] | Conference | 2010 | ACM | 65 |
ID | Name of the Query | Description |
---|---|---|
S2 | Range Friends (RF) | returns the friends of a user within a given range |
Nearest Friends (NF) | returns the nearest friends of a user to a given location | |
Nearest Star Group (NSG) | returns a user group, which (i) forms a star subgraph of the social network, and (ii) minimises the aggregate (Euclidean) distance of its members to a given location | |
S3 S18 | Minimum user spatial-aware interest group query (MUSIGQ) | returns a group of users that have the common interests and stay in the near spots |
S5 | Multiple Userdefined Spatial Query (MUSQ) | returns the best answers for a group of users considering both their locations and non location preferences |
S6 | Circle of Friend Query (CoFQ) | finds a group of friends who are close to each other both socially and geographically |
S7 | Cohesive group nearest neighbor (CGNN) | return a group of attendees such that the travel cost of each attendee is within a range, and the total travel cost of all attendees is minimised |
Cohesive group nearest neighbor queries under multi-criteria (MCGNN) | return a group of attendees and a set of locations such that the travel cost of each attendee is within a range, and the overall scores of locations are maximised under multi-criteria | |
S8 | l-cohesive m-ridesharing group (lm-CRG) | retrieves a cohesive ridesharing group by considering spatial, social, and temporal information |
S13 S54 | Spatial-aware Interest Group (SIG) | retrieves a user group where each user is interested in the query keywords and the users are close to each other in the Euclidean space |
Geo-Social K-Cover Group (GSKCG) | finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points | |
Social-aware Ridesharing Group (SaRG) | retrieves a group of riders by taking into account their social connections besides traditional spatial proximities | |
S14 | Group planning query over spatial-social networks (GP-SSN) | retrieves a group of friends with common interests on social networks and a number of spatially close points of interest (POIs) that best match group’s preferences and have the smallest traveling distances to the group. |
S16 | Reverse nearest neighborhood (RNH) | discovers the neighborhoods that find a query facility as their nearest facility among other facilities in the dataset |
S17 S30 | Spatial Group Preference (SGP) | returns top-k POIs that are much likely to satisfy the group’s preferences for POI categories |
S22 | Geosocial group query | retrieves k users that satisfy the minimum acquaintance constraint and has the minimum spatial distance to the query issuer |
S23 | Geo-Social K-Cover Group (GSKCG) | retrieves a minimum user group in which each user is socially related to at least k other users and the users’ associated regions can jointly cover all the query points |
S29 | Group nearest compact POI set (GNCS) | finds a compact set of POIs that is close to all users |
S31 | Group trip planning (GTP) | returns for each type of data points those locations that minimize the total travel distance for the entire group |
S35 | User community preference query | return satisfied POIs based on semantic spatial information and semantic category preference weights |
S36 | Geo-Social Group preference Top-k (SG-Topk) | returns top-k places that are most likely to satisfy the needs of users based on spatial and social relevance |
S42 S52 | Socio-Spatial Group Query (SSGQ) | select a group of nearby attendees with tight social relation |
S43 | Personalised geosocial group (PGSG) | find a venue and a user group, where each user is socially connected with at least c other users, and the maximum distance of all the users in the group to the venue is minimised |
S46 | Reverse Nearest Social Group (RNSG) | finds all social groups that satisfy k-core constraint and have their farthest member (individual with maximum euclidean distance to the query point) as a reverse nearest neighbor of the query point |
S49 | Skyline cohesive group query | finds a group of users, which are strongly connected and closely co-located |
S52 | Multiple Rally-Point Social Spatial Group Query (MRGQ) | selects an appropriate activity location for a group of nearby attendees with tight social relationships |
S53 | Consensus query | finds a meeting place that minimises the travel distance for at least a specified number of group members |
Constraints | Paper ID | Total | ||
---|---|---|---|---|
Spatial | Distance | Euclidean | S3, S5, S6, S13, S16, S18, S54 | 7 |
No-Euclidean | S2, S17, S22, S23, S30, S35, S36, S42, S52, S43, S46 | 11 | ||
Range | S2, S7, S23 | 3 | ||
Coverage | S13, S54 | 2 | ||
Travel cost | S7, S8, S13, S14, S29, S49, S53, S54 | 8 | ||
Social | Friendship | S2, S29, S31, S36, S53 | 5 | |
Interest/preference score | S3, S5, S13, S14, S17, S18S30, S35, S54 | 9 | ||
Closeness | S6, S7, S16 | 3 | ||
Acquaintance | S8, S13, S22, S23, S42, S43, S46, S49, S52, S54 | 10 | ||
Temporal | S8 | 1 |
ID | Name of the Query | Description |
---|---|---|
S9 S32 S41 | collective spatial keyword query (CoSKQ) | finds a set of objects in the database such that it covers a set of given keywords collectively and has the smallest cost |
S12 | Multiple Reverse Top-k Geo-Social Keyword Query (RkGSKQ) | aims to find all the users who have multiple geosocial objects in their top-k geosocial keyword query results |
S24 | Geo-Social Keyword Skyline Query (GSKSQ) | returns the skyline of a set of PoIs based on a query point, the social relationships of the query owner, and query keywords |
S28 | geo-social top-k keyword (GSTK) | retrieves the k best data objects based on spatial, textual and social relevance |
S28 | geosocial skyline keyword (GSSK) | returns every object within range which is not dominated by any other object in terms of distance to the query location and aggregated score of social and keyword relevance |
S4 S33 S45 | multiple-user location-based keyword (MULK) query | returns a set of POIs that are ’close’ to the locations of the users in a group and can provide them with potential options at the lowest expense (e.g., minimising travel distance) |
S38 | multiple-user closest keyword- set (MCKS) query | searches a set of Points of Interest (POIs) that cover the query keyword-set, are close to the locations of multiple users, and are close to each other |
S39 | Social-based Time-aware Spatial Keyword Query (STSKQ) | returns the top-k objects by taking geo-spatial score, keywords similarity, visiting time score, and social relationship into consideration |
S40 | diversified top-k geosocial keyword (D k GSK) query | returns the top- k objects based on their spatial and textual proximity to q as well as the check-in counts of u ’s friends at such objects |
S44 | Popularity-aware collective keyword (PAC-K) query | finds a group of popular POIs that cover the query’s keywords and satisfy the distance requirements from each node to the query node and between each pair of nodes, such that the sum of rating scores over these nodes for the query keywords is maximized |
S50 | Social space Keyword Query | returns the top-k semantic trajectory for users has higher social relevance and shorter distance while satisfying spatial and keyword constraints |
S56 | why-not top-k geosocial keyword (WNGSK) query | returns the top-k objects based on their spatial and textual proximity to the query location as well as the check-in counts of user’s friends at such objects |
Constraints | Paper ID | Total | |
---|---|---|---|
Spatial | Cost | S9, S32, S41, S38, S44, S50 | 6 |
Distance | S12, S24, S28, S4, S33, S39, S40, S45, S56 | 9 | |
Social | Friendship | S12, S24 | 2 |
Relevance | S28, S40, S50, S56 | 4 | |
Relationship effect | S39 | 1 | |
Collective | S4, S9, S32, S33, S38, S41, S44, S45 | 8 |
ID | Name of the Query | Description |
---|---|---|
S10 | Top-k join queries | compute the k combinations of several query search results over geospatial and social data sources with the highest score |
S11 | Top-k spatio-social Point-of-Interest Queries | rank POIs by a weighted sum of their popularity and proximity |
S12 | Multiple Reverse Top-k Geo-Social Keyword Query (RkGSKQ) | aims to find all the users who have multiple geosocial objects in their top-k geosocial keyword query results |
S25 | Geo-Social Ranking top-k query | ranks the k users with the highest scores computed on their distance to a location, the number of their friends in the vicinity of the location, and possibly the connectivity of those friends |
S27 | Geo-Social Temporal Top-k (GSTTk) | retrieves top-k places (points of interest) ranked according to their spatial, social, and temporal relevance to the query user |
S28 | Geo-social top-k keyword (GSTK) | retrieves the k best data objects based on spatial, textual and social relevance |
S36 | Geo-Social Group preference Top-k (SG-Topk) | returns top-k places that are most likely to satisfy the needs of users based on spatial and social relevance |
S39 | Social-based Time-aware Spatial Keyword Query (STSKQ) | returns the top-k objects by taking geo-spatial score, keywords similarity, visiting time score, and social relationship into consideration |
S40 | Diversified top-k geosocial keyword (D k GSK) query | returns the top- k objects based on their spatial and textual proximity to q as well as the check-in counts of u ’s friends at such objects |
S51 | Top-k famous places (TkFP) | retrieves top-k places (points of interest) ranked according to their spatial and social relevance to the query user |
S56 | Why-not top-k geosocial keyword (WNGSK) query | returns the top-k objects based on their spatial and textual proximity to the query location as well as the check-in counts of user’s friends at such objects |
Constraints | Paper ID | Total | |
---|---|---|---|
Spatial | Distance | S10, S11, S12, S25, S27, S28, S36, S39, S40, S51, S56 | 11 |
Social | Friendship | S12, S25, S27, S51 | 4 |
Popularity | S11 | 1 | |
Relationship effect | S39 | 1 | |
Relevance | S10, S27, S28, S36, S40, S51, S56 | 7 | |
Connectivity | S25 | 1 | |
Temporal | S27, S39 | 2 |
ID | Name of the Query | Description |
---|---|---|
S20 | LBSNs friend recommendation skyline query (LFRSQ) | returns the friend recommendation list by considering three factors: (a) common friend, (b) distance influence, and (c) similarity score, which is calculated from location similarity and friend influence between user and candidate friends |
S26 | Geosocial skyline query | reports for a given user and a given location the pareto-optimal set of persons who are close to the location and closely connected to the user |
S24 | Geo-Social Keyword Skyline Query (GSKSQ) | returns the skyline of a set of PoIs based on a query point, the social relationships of the query owner, and query keywords |
S28 | Geosocial skyline keyword (GSSK) | returns every object within range which is not dominated by any other object in terms of distance to the query location and aggregated score of social and keyword relevance |
S49 | Skyline cohesive group query | finds a group of users, which are strongly connected and closely co-located |
S51 | Socio-Spatial Skyline Query (SSSQ) query | returns every place for which there does not exist any other place that has a better social score and better spatial score |
Constraints | Paper ID | Total | |
---|---|---|---|
Spatial | Distance | S20, S24, S26, S28, S49, S51 | 6 |
Social | Friendship | S24, S51 | 2 |
Influence | S20 | 1 | |
Similarity | S26 | 1 | |
Relevance | S28 | 1 | |
Acquaintance | S49 | 1 |
ID | Name of the Query | Description |
---|---|---|
S2 | Nearest Friends (NF) | returns the nearest friends of a user to a given location |
S7 | Cohesive group nearest neighbor (CGNN) | returns a group of attendees such that the travel cost of each attendee is within a range, and the total travel cost of all attendees is minimised |
Cohesive group nearest neighbor queries under multi-criteria (MCGNN) | return a group of attendees and a set of locations such that the travel cost of each attendee is within a range, and the overall scores of locations are maximised under multi-criteria | |
S15 | k-Relevant nearest neighbor (k-RNN) | retrieves close-by and relevant (as judged by the crowd) POIs |
S16 | Reverse nearest neighborhood (RNH) | discovers the neighborhoods that find a query facility as their nearest facility among other facilities in the dataset |
S19 | kNN and range queries | discover the hot zones (highly populated areas) based on users’ spatial movement patterns and incorporate them into the construction of watchtowers |
S22 | Geosocial group queries | retrieve k users that satisfy the minimum acquaintance constraint and has the minimum spatial distance to the query issuer |
S23 | Geo-Social K-Cover Group (GSKCG) | retrieves a minimum user group in which each user is socially related to at least k other users, and the users’ associated regions can jointly cover all the query points |
S34 | k-nearest neighbor temporal aggregate (kNNTA) query | returns the top-k locations that have the smallest weighted sums of (i) the spatial distance to the query point and (ii) a temporal aggregate on a certain attribute over the time interval |
S46 | Reverse Nearest Social Group (RNSG) | finds all social groups that satisfy k-core constraint and have their farthest member (individual with maximum euclidean distance to the query point) as a reverse nearest neighbor of the query point |
S53 | Consensus query | finds a meeting place that minimises the travel distance for at least a specified number of group members |
Constraints | Paper ID | Total | |
---|---|---|---|
Spatial | Distance | S2, S15, S16, S19, S22, S23, S34, S46 | 8 |
Travel cost | S7, S53 | 2 | |
Social | Relevance | S15 | 1 |
Popularity | S19, S34 | 2 | |
Closeness | S7, S16 | 2 | |
Friendship | S2, S46, S53 | 3 | |
Acquaintance | S22, S23 | 2 | |
Temporal | S34 | 1 |
ID | Name of the Query | Description | Constraints | |||||
---|---|---|---|---|---|---|---|---|
Spatial | Spatio-Temporal | Social | ||||||
Distance | Trajectories | Route | Relationships | Similarity | Trust | |||
S37 | Geosocial moving query | retrieves trajectories, underlying geographical space and social relationships for mass moving objects | √ | √ | X | √ | X | X |
S47 | Moving reverse nearest neighbour (RNN) query | retrieves neighbourhoods that consider the moving query point as the nearest of all the other facilities | X | √ | X | X | √ | X |
S55 | Social trust aware personalised route query (STPRQ) | finds a proper route R from the starting venue to the destination that should pass through several venues of the respective categories and be credible and popular in the social circle of the query user | √ | X | √ | X | X | √ |
ID | Kind of Query Primitives/Algorithms | Approximate Solution | Access Method | Index Name | Kind of Indexing Method |
---|---|---|---|---|---|
S1 | - | - | - | - | - |
S2 | NA | no | non-index | - | - |
S3 | measure and conquer | no | non-index | - | - |
S4 | sorting, pruning | no | index | MRS-tree | hybrid |
S5 | sorting | no | index | MR-tree | spatial-first |
S6 | sorting, pruning | yes, ε-approximate Algorithm | index | R-tree | spatial-first |
S7 | filter | no | index | road network index IRN | hybrid |
S8 | filter, incremental proximity search | no | index | Social-Equipped R-tree | spatial-first |
S9 | best-first search, pruning | yes, √3-factor approximate algorithm | index | IR-tree | spatial-first |
S10 | join, sorting | teta-approximation algorithm | non-index | - | - |
S11 | scoring, filter | no | index | R-tree | spatial-first |
S12 | partitioning, filter | no | index | GIM-Tree | hybrid |
S13 | filter, branch and bound | no | index | SaRtree | hybrid |
S14 | pruning | no | index | IR and IS | spatial-first |
S15 | filter, scoring, pruning | yes, approximate shortest-path methods | index | spatial grid | spatial-first |
S16 | pruning | greedy solutions for approximation | index | R-tree | spatial-first |
S17 | scoring, pruning | no | index | CR-tree | spatial-first |
S18 | branch and bound/measure and conquer | no | non-index | - | - |
S19 | clustering, Dijkstra search | no | index | Watchtower | spatial-first |
S20 | sorting | no | non-index | - | - |
S21 | - | - | - | - | - |
S22 | clustering, pruning | no | index | SaR-tree | hybrid |
S23 | branch and bound, pruning | no | index | SaR-tree | hybrid |
S24 | scoring, pruning | no | index | SKR-tree | spatial-first |
S25 | branch and bound | no | non-index | - | - |
S26 | pruning | yes, social distance approximation | index | R-tree | spatial-first |
S27 | scoring, pruning | no | index | 3D Friends Check-Ins R-tree | social-first |
S28 | scoring | no | index | B-tree | social-first |
S29 | pruning | no | non-index | - | - |
S30 | scoring, pruning | no | index | R-tree | spatial-first |
S31 | best-first search, pruning | no | index | R*-trees | spatial-first |
S32 | best-first search, pruning | yes, ln |q.ψ|-factor approximation | Index | IR-tree | spatial-first |
S33 | clustering, depth- first search | no | index | HI index | hybrid |
S34 | clustering, best-first search | no | index | TaR-tree | spatial-first |
S35 | scoring, pruning | no | index | tR-tree | spatial-first |
S36 | branch and bound | no | index | B+-Tree, Check-In R-Tree, Facility R-Tree | spatial-first |
S37 | NA | no | index | R-tree | spatial-first |
S38 | scoring, pruning | yes, 3-approximation feasible result search algorithm | index | shortest-path tree | spatial-first |
S39 | best first search, pruning | no | index | NETR-tree | hybrid |
S40 | clustering, sorting, pruning | yes | index | GIM-tree | spatial-first |
S41 | scoring, pruning | yes, the approximate algorithm Unified-A | index | IR-tree | spatial-first |
S42 | branch and bound, sorting, pruning | no | index | Social R-Tree | social-first |
S43 | pruning | no | index | enhanced SaR-tree | hybrid |
S44 | clustering, scoring, sorting, pruning | no | index | I 3ndex and nkIndex | hybrid |
S45 | clustering, best-first search | yes | index | IR-tree | spatial-first |
S46 | sorting, pruning | no | index | R*-tree | spatial-first |
S47 | sorting, pruning | no | index | R-tree | spatial-first |
S48 | NA | no | index | k-d tree and quadtree | spatial-first |
S49 | sorting, pruning | no | index | cd-tree | hybrid |
S50 | sorting, pruning | no | index | SIL-Quadtree | spatial-first |
S51 | scoring, pruning, sorting | no | index | FCRTree | hybrid |
S52 | sorting, pruning | no | index | BallTree | spatial-first |
S53 | clustering | no | index | R-tree | spatial-first |
S54 | scoring, pruning, sorting | no | index | IR-tree | spatial-first |
S55 | sorting, scoring | no | index | category-oracle inverted index | hybrid |
S56 | sorting, pruning | no | index | PIM-tree | hybrid |
S57 | - | - | - | - | - |
Metrics | Paper ID | Total |
---|---|---|
Query response time/processing time | S2, S7, S8, S11, S16, S17, S19, S24, S31, S35, S39, S40, S53, S57 | 14 |
Running time | S3, S9, S10, S12, S13, S18, S22, S23, S25, S26, S28, S29, S30, S32, S38, S41, S42, S43, S44, S45, S46, S48, S49, S50, S52, S55 | 25 |
Server CPU time | S4, S5, S6, S14, S15, S27, S33, S34, S37, S47, S51 | 11 |
Client CPU time | S4, S5 | 2 |
Communication overhead | S4, S5 | 2 |
Correctness | S6 | 1 |
Accuracy | S7, S11, S33 | 3 |
Index construction time | S4, S19, S55, S56 | 4 |
Approximation ratio | S9, S32, S38, S41, S45 | 5 |
I/O cost | S12, S13, S14, S15, S22, S27, S28, S31, S34, S36, S39, S50, S51, S53, S54 | 15 |
Pruning rate | S17, S24, S30, S35 | 4 |
Memory space | S47, S55, S56 | 3 |
Dataset | Paper ID | Size | Sources |
---|---|---|---|
Foursquare dataset | S2, S6, S43, S46, S48, S49, S55 | 12,652 users [S2] 20,550 users [S6, S48] 76,503 users [S43, S55] 87,229 users [S46] 2,153,471 users [S49] | Foursquare |
Twitter dataset | S2, S22 | 2,220,627 users | |
Gowalla dataset | S4, S5, S7, S12, S19, S20, S22, S23, S24, S25, S26, S27, S28, S34, S36, S37, S43, S48, S49, S50, S51, S55, S56 | 6,442,892 check-ins 1,280,969 locations 196,591 users | Gowalla, Stanford large network dataset collection |
FB dataset | S7, S42 | 4039 vertices | |
TW dataset | S7 | 17,069,982 vertices | |
Brightkite dataset | S7, S12, S23, S24, S37, S43, S48, S49, S50, S55 | 4,491,143 check-ins 58,228 users | Brightkite |
Orkut dataset | S7 | 3,072,441 vertices | Orkut |
California road network dataset | S7, S31, S49, S53, S19 | 21,048 vertices 62,556 PoIs | California road network |
San Francisco road network dataset | S7, S19 | 174,956 vertices | San Francisco road network |
Florida road network dataset | S7, S29, S38 | 1,070,376 vertices | Florida road network |
Western USA road network dataset | S7 | 6,262,104 vertices | Western USA road network |
BE dataset | S8, S13 | 11,036 vertices | Brightkite in Europe |
GE dataset | S8, S13 | 38,983 vertices | Gowalla in Europe |
BA dataset | S8, S13 | 32,228 vertices | Brightkite in America |
GA dataset | S8, S13 | 49,613 vertices | Gowalla in America |
Hotel dataset | S9, S32, S40 | 20,790 objects | www.allstays.com, (accessed on 22 December 2021) |
Web dataset | S9, S32, S40 | 579,727 objects | WEBSPAMUK2007 and TigerCensusBlock |
GN dataset | S9, S32, S40, S45 | 1,868,821 objects | geonames.usgs.gov, (accessed on 22 December 2021) |
Yahoo! Local Data Set | S10 | 909 locations | Yahoo! Local |
Twitter + Instagram Data Set | S10 | 45,000,000 tweets and posts | Twitter + Instagram |
LAS dataset | S11, S12 | 27,000 points | Yelp in Las Vegas |
Yelp dataset | S39, S40, S56 | 99,798 objects 527,532 users | Yelp Dataset Challenge |
Bri + Cal dataset | S14 | 61,000 vertices | Brightkite + California road network |
Gow + Col dataset | S14 | 70,000 vertices | Gowalla + Colorado road network |
NE dataset | S16 | 123,593 PoIs | TIGER project at the US Census Bureau |
RR dataset | S16 | 257,942 PoIs | TIGER project at the US Census Bureau |
CAS dataset | S16 | 196,902 PoIs | TIGER project at the US Census Bureau |
Beijing dataset | S17, S35 | 607,307 PoIs | Beijing |
Guangzhou dataset | S17 | 551,595 PoIs | Guangzhou |
Dianping dataset | S22, S54 | 2,673,970 users | https://goo.gl/uUV4Wg, (accessed on 22 December 2021) |
Twitter-2010 | S22 | 41,652,098 users | |
Flickr dataset | S29, S38, S44 | 68,776 users | Flickr |
OpenStreetMap dataset | S33, S56 | 41,905 objects | OpenStreetMap |
Weeplaces dataset | S39 | 99,378 objects 16,021 users | Weeplaces |
NA dataset | S19, S40 | 175,813 vertices 58,228 users | North America road network |
USA dataset | S40 | 3,598,623 vertices 81,306 users | United States road network |
Large dataset | S42 | 153,577 users | Foursquare |
Whrrl dataset | S46 | 4871 users | Whrrl |
New York road network dataset | S49 | 264,346 vertices | New York road network |
Northeast USA road network dataset | S49 | 1,524,453 vertices | Northeast USA road network |
DataSet_4SQ | S52 | 153,577 users | Foursquare |
Jiepang dataset | S54 | 353,493 users | Jiepang |
New York City (NYC) dataset | S34 | 72,626 locations | Foursquare |
Los Angeles (LA) dataset | S34 | 45,591 locations | Foursquare |
GS dataset | S34 | 182,968 locations | Foursquare |
Open Challenges | ID | |
---|---|---|
Technological | use of the shortest route, the interest of riders, obstacles on the road, and location uncertainty to enhance the query ridesharing system | S8 |
use of the historical information of each user in the group to automatically setting the group preference and its weight | S17 | |
to allow each user to specify the minimum number of attendees with each attribute value required to be selected | S42 | |
empirical “relevance” assessment of the query results involving real-world data collected from the Web | S15 | |
to adopt deep learning technologies to train knowledge graphs of users, so as to intelligently perceive the preference information of a user community and choose the best POI | S35 | |
development of a corresponding index structure and various query algorithms, and the distributed implementation of a data model using a large-scale graph | S37 | |
to incorporate more sophisticated spatial queries such as skyline and distance-based joins | S22 | |
integration of methods to favor users whose friends are concentrated near the query and to investigate the adaptation of these methods to related application domains, such as spatial-keyword search | S25 | |
to study geo-social top-k collective keyword queries | S28 | |
Privacy-related | to protect the location privacy of users while evaluating GTP queries | S31 |
group planning over privacy-preserved or inconsistent spatial-social networks | S14 | |
to consider a user location as a region instead of a point that is desirable from the standpoint of privacy | S53 | |
Social | to investigate the issue of social trust and how to integrate social trust into geo-social group query | S43 |
to incorporate social relationships as an important criterion in group formation and develop novel query processing techniques | S54 | |
to study the evaluation of social trust in location-based social networks and to seek other approximate algorithms for solving this new problem | S55 | |
to investigate how other social information, such as social relationships between mobile users, can be utilized to speed up spatial query processing | S19 |
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D’Ulizia, A.; Grifoni, P.; Ferri, F. Query Processing of Geosocial Data in Location-Based Social Networks. ISPRS Int. J. Geo-Inf. 2022, 11, 19. https://doi.org/10.3390/ijgi11010019
D’Ulizia A, Grifoni P, Ferri F. Query Processing of Geosocial Data in Location-Based Social Networks. ISPRS International Journal of Geo-Information. 2022; 11(1):19. https://doi.org/10.3390/ijgi11010019
Chicago/Turabian StyleD’Ulizia, Arianna, Patrizia Grifoni, and Fernando Ferri. 2022. "Query Processing of Geosocial Data in Location-Based Social Networks" ISPRS International Journal of Geo-Information 11, no. 1: 19. https://doi.org/10.3390/ijgi11010019