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101
Mining compressed commodity workflows from massive RFID data sets
- Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Novemeber 2006
"... Radio Frequency Identification (RFID) technology is fast becoming a prevalent tool in tracking commodities in sup-ply chain management applications. The movement of com-modities through the supply chain forms a gigantic workflow that can be mined for the discovery of trends, flow corre-lations and o ..."
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Cited by 25 (3 self)
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Radio Frequency Identification (RFID) technology is fast becoming a prevalent tool in tracking commodities in sup-ply chain management applications. The movement of com-modities through the supply chain forms a gigantic workflow that can be mined for the discovery of trends, flow corre-lations and outlier paths, that in turn can be valuable in understanding and optimizing business processes. In this paper, we propose a method to construct com-pressed probabilistic workflows that capture the movement trends and significant exceptions of the overall data sets, but with a size that is substantially smaller than that of the complete RFID workflow. Compression is achieved based on the following observations: (1) only a relatively small minority of items deviate from the general trend, (2) only truly non-redundant deviations, i.e., those that substantially deviate from the previously recorded ones, are interesting, and (3) although RFID data is registered at the primitive level, data analysis usually takes place at a higher abstrac-tion level. Techniques for workflow compression based on non-redundant transition and emission probabilities are de-rived; and an algorithm for computing approximate path probabilities is developed. Our experiments demonstrate the utility and feasibility of our design, data structure, and algorithms.
Efficient Storage Scheme and Query Processing for Supply Chain Management Using
- RFID,” Proc. ACM SIGMOD Int’l Conf. Management of Data
, 2008
"... As the size of an RFID tag becomes smaller and the price of the tag gets lower, RFID technology has been applied to a wide range of areas. Recently, RFID has been adopted in the business area such as supply chain management. Since companies can get movement information for products easily using the ..."
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Cited by 25 (1 self)
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As the size of an RFID tag becomes smaller and the price of the tag gets lower, RFID technology has been applied to a wide range of areas. Recently, RFID has been adopted in the business area such as supply chain management. Since companies can get movement information for products easily using the RFID technology, it is expected to revolutionize supply chain management. However, the amount of RFID data in supply chain management is huge. Therefore, it requires much time to extract valuable information from RFID data for supply chain management. In this paper, we define query templates for tracking queries and path oriented queries to analyze the supply chain. We then propose an effective path encoding scheme to encode the flow information for products. To retrieve the time information for products efficiently, we utilize a numbering scheme used in the XML area. Based on the path encoding scheme and the numbering scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we propose a method which translates the queries to SQL queries. Experimental results show that our approach can process the queries efficiently. On the average, our approach is about 680 times better than a recent technique in terms of query performance.
Finding Popular Categories for RFID Tags
, 2008
"... As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all ..."
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Cited by 22 (9 self)
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As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all the tag data. We propose two algorithms based on the idea of group testing, which allows us to efficiently derive popular categories of tags. We evaluate our solutions using both theoretical analysis and simulation.
Challenges for pervasive rfid-based infrastructures
- In PERCOMW ’07: Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
, 2007
"... The success of RFID in supply chain management is leading many to consider more personal and pervasive deployments of this technology. Unlike industrial settings, however, deployments that involve humans raise new and critical problems related to privacy, security, uncertainty, and a more diverse an ..."
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Cited by 21 (9 self)
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The success of RFID in supply chain management is leading many to consider more personal and pervasive deployments of this technology. Unlike industrial settings, however, deployments that involve humans raise new and critical problems related to privacy, security, uncertainty, and a more diverse and evolving set of applications. At the University of Washington, we are deploying a building-wide RFID-based infrastructure with hundreds of antennas and thousands of tags. Our goal is to uncover the issues of pervasive RFID deployments and devise techniques for addressing these issues before such deployments become common place. In this paper, we present the challenges encountered and lessons learned during a smaller-scale pilot deployment of the system. We show some preliminary results and, for each challenge, discuss how we addressed it or how we are planning on addressing it. 1.
Testing pervasive software in the presence of context inconsistency resolution services
- PROCEEDINGS OF THE 30TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2008)
, 2008
"... Pervasive computing software adapts its behavior according to the changing contexts. Nevertheless, contexts are often noisy. Context inconsistency resolution provides a cleaner pervasive computing environment to context-aware applications. A faulty context-aware application may, however, mistakenly ..."
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Cited by 20 (10 self)
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Pervasive computing software adapts its behavior according to the changing contexts. Nevertheless, contexts are often noisy. Context inconsistency resolution provides a cleaner pervasive computing environment to context-aware applications. A faulty context-aware application may, however, mistakenly mix up inconsistent contexts and resolved ones, causing incorrect results. This paper studies how such faulty context-aware applications may be affected by these services. We model how programs should handle contexts that are continually checked and resolved by context inconsistency resolution, develop novel sets of data flow equations to analyze the potential impacts, and thus formulate a new family of test adequacy criteria for testing these applications. Experimentation shows that our approach is promising.
Efficient Tag Identification in Mobile RFID Systems
"... Abstract—In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first propose a probabilistic model for RFID tag identification. Based on this model, we propose efficient solutions t ..."
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Cited by 20 (10 self)
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Abstract—In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first propose a probabilistic model for RFID tag identification. Based on this model, we propose efficient solutions to identify moving RFID tags, according to the fixed-path mobility on the conveyor. A dynamic program based solution and an adaptive solution are proposed to select optimized frame sizes during the query cycles. Simulation results indicate that by leveraging the probabilistic model our solutions can achieve much better performance than using parameters for the ideal propagation situations. I.
Partial Constraint Checking for Context Consistency in Pervasive Computing
- ACM TRANS. ON SOFTWARE ENGINEERING AND METHODOLOGY 19(3), ARTICLE 9
, 2010
"... Pervasive computing environments typically change frequently in terms of available resources and their properties. Applications in pervasive computing use contexts to capture these changes and adapt their behaviors accordingly. However, contexts available to these applications may be abnormal or imp ..."
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Cited by 17 (8 self)
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Pervasive computing environments typically change frequently in terms of available resources and their properties. Applications in pervasive computing use contexts to capture these changes and adapt their behaviors accordingly. However, contexts available to these applications may be abnormal or imprecise due to environmental noises. This may result in context inconsistencies, which imply that contexts conflict with each other. The inconsistencies may set such an application into a wrong state or lead the application to misadjust its behavior. It is thus desirable to detect and resolve the context inconsistencies in a timely way. One popular approach is to detect context inconsistencies when contexts breach certain consistency constraints. Existing constraint checking techniques recheck the entire expression of each affected consistency constraint upon context changes. When a changed context affects only a constraint’s subexpression, rechecking the entire expression can adversely delay the detection of other context inconsistencies. This article proposes a rigorous approach to identifying the parts of previous checking results that are reusable without entire rechecking. We evaluated our work on the Cabot middleware through both simulation experiments and a case study. The experimental results reported that our approach achieved over a fifteenfold
Heuristics-based strategies for resolving context inconsistencies in pervasive computing applications
- In Proceedings of ICDCS
, 2008
"... Context-awareness allows pervasive applications to adapt to changeable computing environments. Contexts, the pieces of information that capture the characteristics of environments, are often error-prone and inconsistent due to noises. Various strategies have been proposed to enable automatic context ..."
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Cited by 16 (5 self)
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Context-awareness allows pervasive applications to adapt to changeable computing environments. Contexts, the pieces of information that capture the characteristics of environments, are often error-prone and inconsistent due to noises. Various strategies have been proposed to enable automatic context inconsistency resolution. They are formulated on different assumptions that may not hold in practice. This causes applications to be less context-aware to different extents. In this paper, we investigate such impacts and propose our new resolution strategy. We conducted experiments to compare our work with major existing strategies. The results showed that our strategy is both effective in resolving context inconsistencies and promising in its support of applications using contexts.
A Sampling-Based Approach to Information Recovery †
"... Abstract — There has been a recent resurgence of interest in research on noisy and incomplete data. Many applications require information to be recovered from such data. Ideally, an approach for information recovery should have the following features. First, it should be able to incorporate prior kn ..."
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Cited by 16 (5 self)
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Abstract — There has been a recent resurgence of interest in research on noisy and incomplete data. Many applications require information to be recovered from such data. Ideally, an approach for information recovery should have the following features. First, it should be able to incorporate prior knowledge about the data, even if such knowledge is in the form of complex distributions and constraints for which no close-form solutions exist. Second, it should be able to capture complex correlations and quantify the degree of uncertainty in the recovered data, and further support queries over such data. The database community has developed a number of approaches for information recovery, but none is general enough to offer all above features. To overcome the limitations, we take a significantly more general approach to information recovery based on sampling. We apply sequential importance sampling, a technique from statistics that works for complex distributions and dramatically outperforms naive sampling when data is constrained. We illustrate the generality and efficiency of this approach in two application scenarios: cleansing RFID data, and recovering information from published data that has been summarized and randomized for privacy. I.
