Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> Understanding sources of sustained competitive advantage has become a major area of research in strategic management. Building on the assumptions that strategic resources are heterogeneously distributed across firms and that these differences are stable over time, this article examines the link between firm resources and sustained competitive advantage. Four empirical indicators of the potential of firm resources to generate sustained competitive advantage-value, rareness, imitability, and substitutability are discussed. The model is applied by analyzing the potential of several firm resources for generating sustained competitive advantages. The article concludes by examining implications of this firm resource model of sustained competitive advantage for other business disciplines. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> Abstract The immense amount of data generated and collected on e-commerce platforms provides opportunities and challenges for big data analytics to create business value. E-tourism platforms collect not only users’ travel information but also users’ social connection information and need effective personalized recommendation systems for target marketing. In this paper, we aim to study how different types of social relationships such as colleague, schoolmate, and relative between co-travelers influence a user’s travel behavior and how to use this influence to enhance recommendation quality. To this end, we develop a probabilistic topic model leveraging individual travel history and social influence of co-travelers to capture personal interests and propose a recommendation method to utilize the proposed model. Experiments on a real travel dataset show that the proposed approach significantly outperforms benchmarks. The result highlights useful findings for travel agencies. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> Consumer analytics is at the epicenter of a Big Data revolution. Technology helps capture rich and plentiful data on consumer phenomena in real time. Thus, unprecedented volume, velocity, and variety of primary data, Big Data, are available from individual consumers. To better understand the impact of Big Data on various marketing activities, enabling firms to better exploit its benefits, a conceptual framework that builds on resource-based theory is proposed. Three resources—physical, human, and organizational capital—moderate the following: (1) the process of collecting and storing evidence of consumer activity as Big Data, (2) the process of extracting consumer insight from Big Data, and (3) the process of utilizing consumer insight to enhance dynamic/adaptive capabilities. Furthermore, unique resource requirements for firms to benefit from Big Data are discussed. <s> BIB007 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> A link-based approach, named global consistency maximization (GCM) is proposed for opinion classification.The proposed approach achieves higher accuracy than two baseline approaches.Link-based opinion classifiers are robust to a small training sample if selected properly. Social media is a major platform for opinion sharing. In order to better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model, named global consistency maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter data set show that: (1) our global approach achieves higher accuracy than two baseline approaches and (2) link-based classifiers are more robust to small training samples if selected properly. <s> BIB008 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> We propose a filtering model to predict helpfulness of reviews for product design.We provide a way to use the KANO model based on online reviews.We explore how to obtain insights from Big Data through knowledge-based view. Big data commerce has become an e-commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce. <s> BIB009 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. MOBILE/SOCIAL MARKETING <s> Abstract Characterizing and mining people’s individual mobility pattern (IMP) has recently attracted much attention, and has shown great potential significance in many application fields, e.g., transportation planning, activity prediction, supermarket location decision, resource allocation in distributed and communication systems, etc. Social media has been widely regarded as an effective data source for IMP mining as it contains huge records with location and temporal tags. Existing social media based IMP mining methods mainly focus on the spatial aspect (e.g., points-of-interests). Some recent studies argue that, besides spatial aspect, IMP also exhibits temporal feature, and therefore it is more significant to find out the spatial–temporal path (STP). However, we notice that existing studies can only generate single STP for each individual. In practice, there may exist multiple STPs for an individual over a long time period. Regarding such fact, in this paper, we argue that it is more rationale to find out multiple STPs (mSTP) for each individual. While, this is a non-trivial task due to the inherent sparsity and uncertainty in social media based location data. To address this problem, we propose a social media based mSTP mining framework. The spatial–temporal hotspots of individual activities are first clustered. Bayesian theorem is then applied to find out the mSTP amongst the generated hotspots. An mSTP visualization method is also provided to further indicate the relationship amongst the mined mSTP. Experiments with collected trace from Weibo, the biggest social media in China, is conducted to evaluate the validity of the proposed method with respect to several metrics. The experiment results indicate that the our method is able to correctly mine mSTPs consistent with human common sense. <s> BIB010
Marketing activities with social BD have been enhanced to a great extent. Organizations are investing in BD infrastructure by logging and storing data from customer engagements because it helps to achieve a positive return on investment (ROI)  . One way to get high ROI is to perform predictive analytics for product demands which offers accurate demand forecast. According to  , traditional time-series forecasting with historical sales data works well with popular products but is not very predictive for other products because of the random noise, hence more and more firms are relying on predictive analytics from socially generated consumers data. Online social networking websites provide marketers with deep insights and user intelligence to launch targeted marketing campaigns. Similarly, online recommender systems used by YouTube and Netflix as well as e-commerce websites such as Amazon and eBay can significantly transform marketing BIB005 . A study of Twitter as an advertising medium analyzing range frequency, timing, and tweet contents revealed that 19% of tweets from the corporate accounts do mention at least one 'brand'  . He et al. BIB006 argue that in today's competitive environment, BD is one of the vital resources for a firm that is able to generate something of value to its customers and cannot be imitated. According to the resource-based view of a firm, there are three types of resources: physical capital, human capital, and organizational capital BIB001 . In the context of BD, the physical capital is referred as the applications used to gather and analyze BD  . The human capital can be equated to the data analysts who can have insights into the data to know about consumer activities. The organizational capital is basically the structure that enables the strategies to be translated into actions BIB007 . Building on this insight, firms have recently started utilizing consumer BD generated through mobile phones and applications to enhance their dynamic capabilities. One such example is the use of recorded conversations between service staff and customers by 58840 VOLUME 6, 2018 various organizations with the integration of speech-analytics to determine key performance indicators  . Similarly, a much more proactive approach is used by Target stores in competition with Walmart reported by Li et al. BIB008 . With the built-in GPS in mobile devices to enable location information for users, social media applications allow their users to post content with geographic information, hence producing vast amount of data with location information BIB010 . Gonzalez et al. BIB002 coined the term individual mobility pattern (IMP) to understand human behaviors in performing daily activities based on their locations. The main source of data for IMP which is mobile phone trajectory combined with social media data generated by users becomes a competitive data source for moving objects. Naturally, with respect to IMP, spatial aspect remains the most important one e.g., frequent visits to point-of-interest as identified by Yuan et al. BIB003 and the role of GIS as social media by online mapping websites. Therefore, a plenty of research is done on social media and spatial data. However, researchers have recently realized regularities not only in spatial but also in temporal aspects of IMP and hence stressing spatialtemporal data for individuals to be more rational BIB010 . Therefore, in addition to the data related to individuals pointsof-interest, temporal data such as the sequence of visits to these points-of-interest provides us with more comprehensive spatio-temporal data. He et al. BIB006 state that with the growing richness of data, marketers now have more opportunities to identify new gaps which was not possible before. With the availability of geospatial and temporal data, marketers can now predict where a customer/product would possibly be at a given time. Google Now application has the capacity to suggest consumers on the availability of products/services based on their browsing and searching patterns e.g. availability of tickets at a local cinema and movie hours (based on temporal data) or a restaurant offering food according to customer's likings. Text Analytics and Network Analytics in Social Media: One source of SBD (particularly for organizations) is the unstructured textual information residing in email communications, corporate documents, and social media content on websites such as Facebook, Twitter and other social media. Government and research institutions also monitor such textual information for security purposes and/or to analyze the general opinion of public for popular events such as elections and referendums. According to Erevelles et al. BIB007 , in the World Wide Web (WWW), opinion identification and classification is normally based on social media textual contexts, therefore the authors suggest two main approaches for such classification; lexicon and learning based methods. Lexicon-based approaches have pre-established rules to interpret textual sentiments BIB004 such as measuring the tone of news. Learning methods (on the other hand) use a hybrid approach combining lexicons and machine learning to classify opinions (the learning methods are discussed in much detail in section V). Similarly, online reviews of products by people/customers are also a source of data for marketers as well as producers BIB009 . Qi et al. BIB009 further argue that online reviews allow a customer to be a part of product design and development as well as customer requirements. Erevelles et al. BIB007 believe that opinion mining is an extensively used approach in social media environments. Network analytics are often coupled with text analytics in social media to derive more sense out of data. Users' online posts or tweets trigger responses such as likes, comments, and re-shares, which may lead to the population of more textual content. According to Chen et al.  , network analytics focus on link mining and community detection by exploring and predicting the connectivity among nodes of a network. Nodes can be in the form of customers and users of a service or products, while the links represent the medium such as email messages, and social media comments, likes and shares. These connections between nodes create a social network of opinionated users. Online recommendation system such as YouTube video recommendation also utilizes SBD consolidated by network connections by grouping similar users and extending it to other users by suggesting content they might be interested in. For instance, He et al. BIB006 show that an online service for travel recommendation must capture or infer users' travel interests from their consumption behavior on travel packages which is termed as social recommendation. Network SBD analysis is also being utilized in road network modeling for instance in an online taxi service with the use of GPS sensors. Zhou et al.  highlight a problem in traditional taxi service focusing on misbehavior of taxi drivers. These drivers were not taking the shortest route to travel from point A to B, and the passengers were not aware of this deception especially when they were not acquainted with the route. This detouring allows taxi drivers to overcharge passengers in unfamiliar settings. By incorporating GPS sensors in taxis, data such as position, speed, timestamps, and flag markings can be generated. Therefore, Zhou et al.  suggest to prevent this fraud by considering the data of customers who have already traveled from point A to B and hence, consolidating this data to calculate the optimal route. Zhou et al.  further recommend a real-time trajectory detection which means that when a driver tries to deviate the recommended path, a notification may be pushed to the passenger notifying him/her of a potential fraud. We further discuss route recommendation and similar topics in Section IV.