یکپارچه سازی معنایی در داده های دولت برای مدیریت کیفیت آب
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4410||2007||20 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Government Information Quarterly, Volume 24, Issue 4, October 2007, Pages 716–735
Normative models of e-government typically assert that horizontal (i.e., inter-agency) and vertical (i.e., inter-governmental) integration of data flows and business processes represent the most sophisticated form of e-government, delivering the greatest payoff for both governments and users. This paper concentrates on the integration of data supporting water quality management as an example of how such integration can enable higher levels of e-government. It describes a prototype system that allows users to integrate water monitoring data across many federal, state, and local government organizations and provides novel techniques for information discovery, thus improving information quality and availability for decision making. Specifically, this paper outlines techniques to integrate numerous water quality monitoring data sources, to resolve data disparities, and to retrieve data using semantic relationships among data sources taking advantage of customized user profiles. Preliminary user feedback indicates that these techniques enhance quantity and quality of information available for water quality management.
Normative models of e-government typically assert that horizontal (i.e., inter-agency) and vertical (i.e., inter-governmental) integration of data flows and business processes represent the most sophisticated form of e-government, delivering the greatest payoff for both governments and users. With this sophistication, though, comes great complexity of design and implementation that spans the domains of information systems, information policy and public administration. This paper concentrates on the integration of data supporting water quality management as an example of how it is possible to address this complexity in setting and implementing water quality policy. Data integration problems arise from the implementation of the Federal Clean Water Act (CWA, under Sections 303(d) and 305(b)), which requires states, territories, and authorized tribes to report on the water quality status of jurisdictional waters every 2 years. These CWA mandates create unique data needs and problems, such as how to interpret information derived from multiple sources, of variable quality, using different formats, and collected according to different protocols and procedures. Existing tools for managing these data are not integrated nor do they provide any sort of data analysis capability to allow water resource managers to make informed decisions. The combination of both organizational and data complexity creates fundamental challenges to developing policies, based upon a robust information stream, which are responsive to a wide range of stakeholder interests. Such a problem setting represents the kind of challenge that sophisticated e-government systems are supposed to address. We describe an interoperable system that builds upon the current digital government literature, allowing users to integrate water monitoring data across many organizations into a data warehouse for subsequent knowledge discovery. Our system makes the following contributions: 1.1. Strikes a balance between breadth and depth in data integration Numerous organizations and individuals (e.g., volunteers) collect water quality data. Ideally we should integrate all possible data into a uniform format, but in practice this is difficult to accomplish because data sources do not agree on a universal format. We describe a hybrid approach that integrates the metadata (including information on how to access data sources, when and where the data are collected, which parameters are monitored, etc.) of all data sources but fully integrates data only from key sources, such as data from the Environmental Protection Agency (EPA) or U.S. Geological Survey (USGS). Thus, we enable water quality managers and policy makers to search the integrated metadata, to locate any source of interest, and to manually download data from that source; or access the fully integrated data as if the data were from a single source. 1.2. Enables policy makers to retrieve customized views of water quality data Many aspects of water quality decision making (i.e., optimizing policy outcomes and minimizing public costs) depends on complete and consistent data. The decision making process, includes many diverse interests, including government agencies with different oversight and regulatory responsibilities. This leads to disparate, incomplete, and unintegrated data stores on water quality, which may result in sub-optimal decisions based on incomplete or poor quality data. This prototype recognizes the need for different stakeholder views for both data input and output in the decision making process. Establishing user profiles through the data retrieval interface allows different stakeholders and decision makers to customize data queries consistent with their interest or legal and policy responsibilities. Preliminary feedback from users indicates that such customized queries based on profiles and the other tools discussed below increases the breadth, depth, and quality of water quality data available for decision making. 1.3. Exploits semantic relationships Identifying a water quality problem often requires analysis of data from multiple sources that are related semantically. For example, users looking for possible explanations for a recent change in fish population in a stream may need to examine all data sources semantically related to fish population, such as stream temperature, impervious surfaces, elevation, land cover, etc. It would be tedious and impractical for users to find all this information by themselves. We represent such relationships among data sources using semantic networks, which assist users in locating related sources. 1.4. Resolves disparities in resolution of spatial, temporal, and format Water monitoring data are collected in a variety of formats, units (metric or SI), and spatial and temporal granularities. For example, one data set may measure stream flow in cubic feet per second while another data set uses cubic meters per second. Furthermore, land cover data are typically captured at a 30-meter resolution, but stream chemical and biological data are collected at a specific point along a stream segment. This leads to disparities in terms of spatial granularities. We resolve format disparities through the development of conversion tools (which convert one format into another) and we resolve the spatial and temporal disparities using spatial–temporal join/aggregation operations (which aggregate lower level location areas into higher level ones). We describe the conceptual model of a data warehouse to store the fully integrated water quality data for advanced decision support to assist water quality managers. The remainder of the paper is organized as follows. In Section 2, we provide information related to the government mandate for water quality management, and in Section 3, we discuss related work in e-government and data integration. Section 4 presents our methodology and technical approach to using data integration to support policy makers. Section 5 describes the usability study findings of users who experimented with the prototype system for water quality management. The final section, Section 6, summarizes our findings from this research and identifies future work.
نتیجه گیری انگلیسی
Data integration and systems interoperation are challenging but necessary tasks for government agencies and water monitoring councils that need to identify pollution sources and give the public access to such information. In this paper, we discussed how a prototype system allowed users to integrate water monitoring data across many government organizations and use the subsequent data integration and an enhanced interface for data retrieval to provide better information for water quality monitoring and related decision making. The prototype used a dual approach to data integration, including both a shallow and deep level. This approach automatically identifies semantically relevant yet unknown information and supports the creation of user profiles for possible expansion of the high level queries with the ability to refine the profiles over time. Preliminary feedback from a usability study indicated that the prototype delivered improved data query capabilities and the users rated five measured categories quite high. Improvements included a wider array of data sets, customized views with the ability of the user to select or ignore related sets of data depending on their task or stakeholder position and a richer, data warehouse that brought together previously disparate, and unintegrated data sources. As is the case with most prototypes there is more work to be done. Some of this work responds to the emerging and evolving needs of users as they gain familiarity with the capabilities of the system. As part of our continuing work, and based on the feedback of users, we will improve our system to provide an enhanced view of information through visualization and analysis tools. Such additions expand the information that can be presented to decision makers, provide them with additional knowledge, and enable better water management policy. We also plan to evolve our system beyond the prototype status, construct it more robustly, and make it available to the public. There is also obviously a need to expand user testing and build on the existing profiles and related semantic networks. Also we plan to extend the use of the system beyond just the state of Maryland and incorporate metadata for additional states. This will not be difficult to do regarding concepts, but it will take time to collect the metadata and enable shallow and deep integration. At the outset of the paper, a statement of the challenges of making informed decisions on water quality policy underscored the need to rationalize the various sources, sets, and levels of data that are available to policy makers in this domain. The review of the literature of e-government and data integration established a linkage between advanced levels of e-government and underlying data integration for users external to government, stakeholders, and policy makers. The prototype system described in this paper documents a technical approach to data integration to support both monitoring and related policy making for water quality in Maryland. Preliminary user testing has demonstrated some of the potential benefits from more advanced levels of e-government, both vertical and horizontal integration, identified in Layne and Lee's (2001) four-state model of e-government. While this is clearly a first step in a much longer journey, the paper documents that is possible to achieve data integration as a means to delivery improved e-government information products and services.