A Fast and Memory-Efficient Approach to NDN Name Lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities clearly

Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price,the Chinese government has been exploring the most effective policies for regulating the housing market.Measuring the effect of real estate regulation policies has been a challenge for present studies.This study innovatively employs big data technology to obtain Internet search data(ISD)and construct market concern index(MCI)of policy,and hedonic price theory to construct hedonic price index(HPI)based on building area,age,ring number,and other hedonic variables.Then,the impact of market concerns for restrictive policy,monetary policy,fiscal policy,security policy,and administrative supervision policy on housing prices is evaluated.Moreover,compared with the common housing price index,the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand.The results indicate that(1)a long-term interaction relationship exists between housing prices and market concerns for policy(MCP);(2)market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect.The results could serve as a useful reference for governments aiming to stabilize their real estate markets.

Similarity Search Algorithm over Data Supply Chain Based on Key Points

In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points,which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.

Visualization and level-of-detail of metadata for interactive exploration of Sensor Web

There are several issues with Web-based search interfaces on a Sensor Web data infrastructure.It can be difficult to(1)find the proper keywords for the formulation of queries and(2)explore the information if the user does not have previous knowledge about the particular sensor systems providing the informa-tion.We investigate how the visualization of sensor resources on a 3D Web-based Digital Earth globe organized by level-of-detail(LOD)can enhance search and exploration of information by easing the formulation of geospatial queries against the metadata of sensor systems.Our case study provides an approach inspired by geographical mashups in which freely available functionality and data are flexibly combined.We use PostgreSQL,PostGIS,PHP,and X3D-Earth technologies to allow the Web3D standard and its geospatial component to be used for visual exploration and LOD control of a dynamic scene.Our goal is to facilitate the dynamic exploration of the Sensor Web and to allow the user to seamlessly focus in on a particular sensor system from a set of registered sensor networks deployed across the globe.We present a prototype metadata exploration system featuring LOD for a multiscaled Sensor Web as a Digital Earth application.

Analyses of Political Crisis Impact on Tourism:A Panel Counterfactual Approach with Internet Search Index

Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers

This paper proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data,namely,historical tourism volume,newly confirmed cases in tourist origins and destinations,and search engine data.This paper introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence.To solve the problem of insufficient interpretability of existing tourism demand forecasting,this paper also proposes a novel interpretable tourism demand forecasting model called JADE-TFT,which utilizes an adaptive differential evolution algorithm with external archiving(JADE)to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers(TFT).The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macao tourism data sets.The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists'concerns about travel in the post-pandemic era,and the two-stage decomposition method can effectively identify the inflection point of tourism prediction,thereby increasing the prediction accuracy of tourism demand.