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Full-Text Articles in Physical Sciences and Mathematics
Libraryguru: Api Recommendation For Android Developers, Weizhao Yuan, Hoang H. Nguyen, Lingxiao Jiang, Yuting Chen
Libraryguru: Api Recommendation For Android Developers, Weizhao Yuan, Hoang H. Nguyen, Lingxiao Jiang, Yuting Chen
Research Collection School Of Computing and Information Systems
Developing modern mobile applications often require the uses of many libraries specific for the mobile platform, which can be overwhelmingly too many for application developers to find what are needed for a functionality and where and how to use them properly. This paper presents a tool, named LibraryGuru, to recommend suitable Android APIs for given functionality descriptions. It not only recommends functional APIs that can be invoked for implementing the functionality, but also recommends event callback APIs that are inherent in the Android framework and need to be overridden in the application. LibraryGuru internally builds correlation databases among various functionality …
Fast Nearest Neighbor Search With Keywords, Ramu Anthati, Santosh Aditya Kokku, Tejaswini Vodapally
Fast Nearest Neighbor Search With Keywords, Ramu Anthati, Santosh Aditya Kokku, Tejaswini Vodapally
All Capstone Projects
Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects’ geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently the best solution to such queries is based on the IR2-tree, which, as shown in this paper, has a …
Simapp: A Framework For Detecting Similar Mobile Applications By Online Kernel Learning, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao
Simapp: A Framework For Detecting Similar Mobile Applications By Online Kernel Learning, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
With the popularity of smart phones and mobile devices, the number of mobile applications (a.k.a. "apps") has been growing rapidly. Detecting semantically similar apps from a large pool of apps is a basic and important problem, as it is beneficial for various applications, such as app recommendation, app search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem. In order to fill this gap, in this paper, we explore multi-modal heterogeneous data in app markets (e.g., description text, images, user reviews, etc.), and present "SimApp" -- a novel framework for detecting similar …
Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
A common problem in large urban cities is the huge number of retail options available. In response, a number of shopping assistance applications have been created for mobile phones. However, these applications mostly allow users to know where stores are or find promotions on specific items. What is missing is a system that factors in a user's shopping preferences and automatically tells them which stores are of their interest. The key challenge in this system is twofold; 1) building a matching algorithm that can combine user preferences with fairly unstructured deals and store information to generate a final rank ordered …
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Kyriakos MOURATIDIS
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without …
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Research Collection School Of Computing and Information Systems
With the prevalence of mobile devices that are equipped with wireless Internet capabilities and Global Positioning System (GPS) functionality, the creation and access of user-generated content are extended to users on the go. Such content are tied to real world objects, in the form of geospatial annotations, and it is only natural that these annotations are visualized using a map-based approach. However, viewing maps that are filled with annotations could hinder the serendipitous discovery of data, especially on the small screens of mobile devices. This calls for a need to manage the annotations. In this paper, we introduce a mobile …
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Preventing Location-Based Identity Inference In Anonymous Spatial Queries, Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias
Research Collection School Of Computing and Information Systems
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without …