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Approximate Top-k Queries Monitoring on Document Streams Abstract Document stream is the stream where documents are flows continuously

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Approximate Top-k Queries Monitoring on Document Streams

Abstract
Document stream is the stream where documents are flows continuously. By monitoring these documents it is possible to have different applications of the real world like demand presentation, contextual advertisements, filtering of news updates, and general filtering of information to meet the needs of users. User preferences are used to process the top-k monitoring of documents streams continuously. However, it is a tedious and challenging task to fulfil the aspirations of various users and their preferences. In the literature many solutions are found. However an adaptive approach is essential to achieve better results. In this paper we proposed a framework and implemented to have continuous monitoring and approximation of document streams to Top-k queries of different users. Thus the proposed system yields more utility to end users than existing system. Top-k queries instead of preferences can provide the intent of users more clearly. Thus the filtered documents can reveal the user intention in making such queries. An algorithm named Adaptive Identifier Ordering (AIO) is implemented to achieve this. AIO adapts to the runtime dynamics of streaming besides using top-k queries to reports users with most appropriate documents. We build a prototype application to demonstrate proof of the concept.

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Keywords – Document streams, top-k queries, continuous monitoring, adaptive identifier ordering

INTRODUCTION
Data in the form of document is growing exponentially. It is evident in the era of big data where data is voluminous, streamed continuously with variety of data. Document streaming is the concept in which documents arrive to server from different sources. Making queries on the streams can help in obtaining information that is latest with high coverage. Social networking web sites like Twitter are generating text documents continuously. Processing such continuously streaming data is challenging but it bestows plethora of benefits. In order to understand the dynamics of document streaming, literature review is made which revealed different existing methods. Various filtering algorithms are explored in 1-3 and top-k queries are studied in 5, 6, 7, 15 and 17.
The top-k queries on the document streaming is made in 22 where revere ordering techniques like RIO and MRIO are explored. However, we considered continuous monitoring and making top-k queries on document streaming an optimization problem and proposed a method known as Adaptive Identifier Ordering (AIO) which is adaptive in nature and suitable for continuous monitoring of document streams. Our contributions in this paper are as follows.
We built a new algorithm known as Adaptive Identifier Ordering (AIO) for continuous monitoring of top-k queries on document streams. This algorithm is adaptive in nature and found to be effective in making top-k queries on document streams.
We built a prototype application to demonstrate proof of the concept. The application has web based intuitive interface while the business logic is built in the server which provides response to user queries. The application is a web client from which user makes queries.
We evaluated the proposed algorithm and found it to have better performs when compared with other state-of-the-art algorithms.
The remainder of the paper is structured as follows. Section 2 provides review of literature. Section 3 presents the proposed system. Section 4 presents implementation details while section 5 covers the experimental results. Section 6 concludes the paper besides providing directions for future work.

RELATED WORK
This section provides review of literature on document streams and making top-k queries in such streams. For effective document retrieval different filtering techniques are explored in 1, 2 and 3. Content matching approach for document retrieval in the domain of publisher/subscriber is investigated in 4. On the other hand top-k matching in the systems that are based on publisher/subscriber is explored in 5. Finding k most relevant publications in the publisher/subscriber model is studied in 6. With respect to social annotation based news, in the context of publisher/subscriber, a system is proposed in 7 for top-k publishing. Many top-k query processing techniques are discussed in 8. Sometimes, it is better to aggregate results so as to make it more meaningful. Such work is done in 9 for making middleware software more useful. Working with multiple aggregations with respect to streaming documents is made in 21.
Text mining and performing inverted search operations is the main focus of the study made in 10. There are two-level procedures followed in 11 for evaluating queries processing. With respect to top-k queries, evaluation procedures are discussed in 12. With respect to sliding windows, the problem of continuous top-k monitoring is explored in 13. Top-k queries with user preferences is investigated in 14 for continuous performance with respect to top-k queries. Evaluation of such continuous top-k queries is made in 15 and 16. With respect to document streaming and personalized query processing is investigated in 17 by using web 2.0 streams. Monitoring of personalized hot news in the context of web 2.0 document streams is made in 18. Documents with non-homogenous scoring functions are studied for processing continuous queries is made in 19 while approximate top-n queries on the documents streams are the study in 20. In 22 a technique known as reverse identifier ordering is employed and they improved it to have efficient top-k queries and monitoring on document streams. From the review of literature it is understood that there has been considerable research on top-k queries on document streams. In this paper we found the suitability of adaptive approach to improve it further by considering it as an optimization problem.

PROPOSED SYSTEM
This section provides the problem formulation, the purpose of the proposed system, methodology followed to solve the problem besides an algorithm that is used to achieve the solution.
A) Problem Definition
Document streams are the streams of documents that continuously flow into a system. Unlike normal documents, the document streams are dynamic in nature and efficient processing of such documents is challenging. Defining similarity metric that is required by documents and queries is another important aspect to be considered. Provided set of documents D and set of queries Q, continuous top-k queries and monitoring them is the problem is to be addressed.
B)Purpose of the System
In the proposed system, a framework is designed and implemented to have continuous monitoring and approximation of document streams to Top-k queries of different users. Thus the proposed system yields more utility to end users than existing system. Top-k queries instead of preferences can provide the intent of users more clearly. Thus the filtered documents can reveal the user intention in making such queries. An algorithm named Adaptive Identifier Ordering (AIO) is implemented to achieve this. AIO adapts to the runtime dynamics of streaming besides using top-k queries to reports users with most appropriate documents. We build a prototype application to demonstrate proof of the concept.
C) Methodology
The methodology followed in the proposed system is as follows. Set of documents that arrive as a stream is denoted as D={d1, d2, …, dn}. Each document has number of terms. The terms are denoted as T={t1, t2, …, tn}. Each term is associated with a weight denoted as f. Adaptive Identifier Ordering (AIO) is the algorithm proposed to have continuous monitoring of document streams with top-k queries. Each document has its ID denoted as dID. A set of words in dictionary is used to make a set of lists. Each term has its own list Li containing (dID, fi). A set of queries given by users is denoted as Q={q1, q2, …, qn}.
D) Adaptive Identifier Ordering
This algorithm is meant for achieving top-k results from document streams. It takes set of documents, set of queries and dictionary word collection as input and produces top-k results for each query in the given set of queries.
Algorithm: Adaptive Identifier Ordering (AIO)
Input: Set of documents D streamed at server, set of queries Q, Dictionary W
Output: Top k results for each query

Initialize vector for list L
Preparing for ID Ordering
For each word w in W
For each document d in D
For each query q in Q
Compute TF-IDF fi for w
Update list L with dID and fi for adapting
Save L
End For
End For
End For
Finding Top-K Results
For each query q in Q
Sort all lists based on ID
Find the average weights
Display top k results for query q
End For
The AIO algorithm takes the streamed documents, set of user queries and set of dictionary words. For each dictionary word, it computes set of lists and finally finds top k documents based on the relevancy. The relevancy is computed using TF-IDF approach of Okapi BM25. Similarity between a query q and the document d is computed as in Eq. 1 according to cosine similarity measure.
C (q, d) = (q . d)/(?q??d? ) = ?_(1?i??T?)??Wi fi? (1)
With the help of similarity measure, it is easier to find out similarity of documents based on given user queries. Moreover the proposed approach is adaptive in nature which continuously adapts to the new dynamics of documents and queries.
IMPLEMENTATION DETAILS
Implementation in the form of client application is made with web based interface. The application demonstrates proof of the concept. The classes involved in the application are administrator, user, and web server. The outline of these roles is as presented in Figure 1.

Figure 1: The classes and the outline of relationship among them
As presented in Figure 1, it is evident that the admin role has activities like adding content, viewing content, viewing search history, and finding all users associated. The end user role has support for operations like query search on document title, query search on domain and finding recommendations or results of queries. These two roles do have interaction with login and registration objects. The data flow among the objects is as shown in Figure 2.

Figure 2: Shows communication flow among the objects
As presented in Figure 2, it is evident that the system support query search on documents while users perform actual search operations. The admin can perform admin activities and controlling the document streams and users. The users and admin users can make use of the system with respective operations. It is role based and permit only intended operations to the roles involved in the system. It is intuitive in nature and users who login can see only the related operations required by them. Thus the system is able to provide functional requirements and also non functional requirements like security and usability.

Figure 3: Interaction among the objects involved
As shown in Figure 3, it is evident that there is interaction among the admin, end user and web server. Since it is a web based application and the business logic is implemented in the server, there is interaction among the objects and the server has to give the results. The implementation is actually made with three tier architecture. The three tiers are known as client tier (browser where web client application runs), web tier (web server like Tomcat Server/Glassfish Server) and data tier like the document base. The algorithm proposed in this paper is implemented as part of the server side functionality.
V. EXPERIMENTAL RESULTS
Experiments are made with different datasets like Wiki-Uniform and Wiki-Connected. More details on these datasets can be found in 22. The observations made in the experiments include number of queries versus response time of the system and length of the queries versus response time.
A) Response Time Based on Number of Queries
Response time is observed based on number of queries made. As the number of queries is increased, the response time taken for RIO, MRIO and proposed AIO are observed and compared.

Figure 4: Number of queries versus the response time (Wiki-Uniform)
As shown in Figure 4, it is evident that the number of queries is taken in horizontal axis while the vertical axis shows the response time observed against the number of queries. The response time of the RIO is less than that of MRIO. The proposed algorithm that is AIO outperforms both ROI and MRIO. As the number of queries is increased, the response time is also increased with linear relationship. These results are captured with the dataset Wiki-Uniform.

Figure 5: Number of queries versus the response time (Wiki-Connected)
As shown in Figure 5, it is evident that the number of queries is taken in horizontal axis while the vertical axis shows the response time observed against the number of queries. The response time of the RIO is less than that of MRIO. The proposed algorithm that is AIO outperforms both ROI and MRIO. As the number of queries is increased, the response time is also increased with linear relationship. These results are captured with the dataset Wiki-Connected.
B) Response Time Based on Length of Queries
Response time is observed based on length of queries. As the length of queries is increased, the response time taken for RIO, MRIO and proposed AIO are observed and compared for both the datasets.

Figure 6: Length of queries versus the response time (Wiki-Uniform)
As shown in Figure 6, it is evident that the length of queries is taken in horizontal axis while the vertical axis shows the response time observed against the length of queries. The response time of the RIO is less than that of MRIO. The proposed algorithm that is AIO outperforms both ROI and MRIO. As the number of queries is increased, the response time is also increased with linear relationship. These results are captured with the dataset Wiki-Uniform.

Figure 7: Length of queries versus the response time (Wiki-Uniform)
As shown in Figure 7, it is evident that the length of queries is taken in horizontal axis while the vertical axis shows the response time observed against the length of queries. The response time of the RIO is less than that of MRIO. The proposed algorithm that is AIO outperforms both ROI and MRIO. As the number of queries is increased, the response time is also increased with linear relationship. These results are captured with the dataset Wiki-Uniform.

VI. CONCLUSIONS AND FUTUE WORK
In this paper, document streams and the process of top-k queries and continuous monitoring of such system are explored. It is found that making top-k queries on continuous document streams is very challenging. In the literature different approaches are found to deal with document streams. However, an adaptive approach that continuously monitors document steams with queries is proposed in this paper. We studied RIO and MRIO methods presented in 22. Inspired by the work, we proposed an algorithm known as AIO that follows an adaptive approach. We built a prototype application and implemented the algorithm as part of a three-tier web application. Web interface is used to make experiments. However, the algorithm lies in server and the streaming takes place in server. End users can make queries from the web based interface in order to observe the response time in presence of number of queries and different length in the queries involved. The experimental results revealed the significance of AIO and its performance improvement over other state-of-the-art algorithms. In future we intent to improve the AIO method with new programming paradigm such as MapReduce with distributed programming frameworks like Hadoop.

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