1. Presented by: Shahab Helmi Spring 2016

2. Authors: Publication:  ICDE 2015 Type:  Research Paper 2

3.  This paper presents Searchlight, a Continuous Query Processing Framework (CQPF) enabling context-aware Continuous Query Processing (CQP) of both actual and predicted movement of objects in symbolic spaces.  The heart of Searchlight is a novel graph-based symbolic space model, the Searchlight Graph (SLG), capable of modeling object movement in both indoor and outdoor symbolic space, and including the novel concept of object and location keywords for capturing rich contextual information and enabling context-aware (predictive) querying. 3

4. A vertex-labeled directed graph represented as (V, E, K, L ids, O, O ids, S, f p, T)  The vertices V represent symbolic locations in indoor or outdoor space. v = ( l id, K l ): location ID and a set of keywords, i.e. Parking, Canteen and …  The edges E are directed and represent possible movements between vertices. An edge e = (V i, V j, C map ) is composed of the source vertex V i, the target vertex V j, and an edge weight function C map that maps a set of keywords and a time instance to a weight denoting the travel time in seconds.  The moving objects in the graph are O. A moving object o = (O id, K O, H, P). Object ID, some keywords, such as “wheelchair-user”, A sequence of location history and anticipated relative future movement. 4

5.  Different departments are shown with different shades of gray.  Dashed vertices are outdoor locations.  The prefix letters of the location ids show the entity type, e.g., A is an auditorium and CT is a canteen.  Edges are directed, to model, e.g., that the entrances E2 and E3 can only be accessed through unidirectional automatic doors from CT2 and P2, respectively. 5

6. Queries can be expressed using the declarative Searchlight Query Language (SLQL).  Range Query: An example range query is to continuously monitor which security guards have been visiting the Math Department at Aalborg University during the past four hours.  Aggregate Query: An example aggregate query is to continuously monitor how many people are predicted to be located in the Biology Department at Aalborg University during the next 60 minutes.  Position Query: continuously monitor the current location of each disabled person inside the Department of Computer Science at Aalborg University. 6

7.  Position Query: report the location of disabled students in the CS department every 5 seconds: 7

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9. Presented by: Dardan Xhymshiti Spring 2016:

10. Authors: Publication:  ICDE 2015 Type:  Research Paper 10

11.  Nowadays location-based social networks are becoming abundant sources of geo- related information.  Geographical location is a new way of connecting people.  Using tweets to retrieve information from them.  The authors defined an approach to use tweets for finding top-k local users who are familiar with relevant issues queried in a certain spatial region. People in need can directly communicate with those recommended local users on Twitter platform. 11

12.  Traditional IR techniques are used to retrieve information from long textual documents rich in keywords. They are not suitable for searching short-sized social media data that are characterized by few keywords.  Twitter offers a search service for searching the top ranked tweets. But this search does not handle spatial aspects.  There existed a search engine Aardvark who retrieved tweets based on querying keywords. This approach returns too many results. 12

13.  The authors defined the top-k local user search query (TkLUS).  General example: Given a location q, a distance r, and a set of words W, the TkLUS query finds the top-k users who have posted tweets relevant to the desired keywords in W at a pace within the distance r from q.  The authors propose two local user ranking methods that integrate text relevance and location proximity in TkLUS query.  They construct a hybrid index under a scalable framework, which is aware of keywords as well as locations to organize high volume of geo-tagged tweets.  The authors devise two algorithms for processing TkLUS queries.  The authors conducted experiments on real Twitter data sets. 13

14. Presented by: Elban Avdylaj Summer 2016

15. Authors: Publication:  ICDE 2015 Type:  Research Paper 15

16.  Internet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information.  SNPs have two unique characteristics: frequent content update and small world phenomenon.  A social network exhibits the small-world phenomenon if any two individuals in the network are likely to be connected through a short sequence of intermediate acquaintances.  Existing works are not able to support these two features simultaneously. 16

17.  To address this problem, the authors develop a general framework to enable real time personalized top-k query.  This framework incorporates time freshness, social relevance and textual similarity 17

18.  Search on Social Media Platform: Facebook developed Unicorn to handle large-scale query processing. While it supports socially related search, the feature is only available for predefined entities rather than for arbitrary documents. Twitter’s real time query engine, Earlybird, has also been reported to offer high throughput query evaluation for fast rate of incoming tweets. Unfortunately, it fails to consider social relationship. 18

19.  Search on Social Network: Several research works have been proposed for real time search indices over SNPs. However, none of them offers customized search for the query user. 19

20. 20  To ensure efficient update and query processing, there are two key challenges. 1. Design an index structure that is update-friendly while supporting instant query processing. 2. Efficiently compute the social relevance in a complex graph.  To address these challenges, the authors first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then they devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, they optimize the 3D index via a hierarchical partition method to enhance their pruning on the social dimension.

21. 21  Twitter - 17M users, 476M tweets  Memetracker - 9M media and 96M records.