This chapter has also discussed the methods that are available for measuring the performance fac- tors, with a focus on two specific categories of measures: measuring disk IO and measuring overall query performance. Although some methods have been found for measuring disk IO in conjunction with database queries there are several downsides that make this a cumbersome process to use in this research, namely (i) disk IO measures are schema and database dependent, which makes any cross-schema or cross-database comparisons impossible and (ii) many IO measures are specific for a specific database product. To measure the overall performance of a query only one method has been found and that is to measure the execution time (in number of seconds) of the query until results are returned, although this can be done using two different approaches: a white-box approach, whereby the execution time is measured by the database itself, or a black-box approach, whereby the execution time is measured by an external client. The second approach suffers from external factors, such as network speed, and is therefore less reliable. However the white-box approach does require that a database is able to time its own queries.
The core query evaluation engine of ADP is built on top of SQLite 4 . The system allows rapid development of specialized data analysis tasks that are directly integrated into the system. Queries are expressed in SQL extended with UDFs. Although UDFs are supported by database management systems (DBMS) for a long time, their use is limited due to their complexity and limitations imposed by the DBMSs. One of the goals of the system is to eliminate the effort of creating and using UDFs by making them a first class citizens in the query language itself. SQLite natively supports UDFs implemented in C. The UDFs are categorized into row, aggregate, and virtual table functions.
We started with star-shaped queries, an essential yet already challenging structural category.
With experimental validation currently underway, we noted that there is no benchmark for SPARQL recommendation, therefore we set out to publish one to complement our evaluation.
As for extending the method itself, there are several directions to take. One is to extend the structural analysis to other classes of queries, such as snowflake and chain. Another one is of course to introduce other types of analysis (such as those based on semantic alignments or subsumption hierarchy) and measure if our structural analysis aids the convergence to better recommendations sooner than without it. Having multiple analysis techniques will also allow a comparative evaluation of the method’s efficiency. Finally, we will keep refining the structural analysis method itself, especially considering other types of operation for generalizing and specializing queries, and possibly taking FILTER clauses into account.
consider that the best set of configuration settings is usually chosen at the deployment time per workload rather than per each input query. Second, we assume that data distribution of the inputs does not change. Increasing the table sizes maintains the relative distribution of values constant, i.e. all datasets sample data from the same distribution. An important effect of this assumption is data proportionality. I.e., for an input schema, the average record size remains constant. We experimentally validated the last assumption on the work- loads that we investigated, which were typically composed of multiple UDFs that were executed on semi-structured data. However, if the data distribution assumption does not hold for workloads which store data in more traditional, structured format, orthogonal approaches may be employed to build histograms on the columns of interest. For instance, online aggregation techniques as proposed in  may be used to build approximate histograms at a low cost.
In this paper we presented how tags from social book- marking system can be exploited to produce query sugges- tions for a specialized group of users using a set of seed web resources and a biased random walk based on point- wise KL divergence between a foreground model and back- ground model. Our method can be used to improve current search assistance functionality for children since we show that our method performs the best for queries aimed at the the youngest group of users (i.e. children between 10 to 12 years old). We show that our method clearly outperforms state of the art search engine query suggestions for this type of queries. We also showed that social media is a highly valu- able resource for the generation of query suggestions and its use can replace the utilization of query logs which may not be available to several search systems. For future work we are interested in applying the method proposed in different domains and on different age segments. We are also inter- ested in enriching our query suggestion method by combin- ing it with other topical features. This can be achieved in a LTOR approach by using our method as one of the features.
This thesis investigates an extension of a fragment of SPARQL 1.1 to include flex- ible querying processing, focusing particularly on its property path queries. The flex- ible querying techniques we investigate are those of  where the authors proposed two flexible querying operators, APPROX and RELAX, for regular path queries. The APPROX operator edits a property path in a query by inserting, deleting or replacing properties, which makes it an ideal candidate for adding to SPARQL 1.1 since the latter supports property path queries. The RELAX operator undertakes ontology-driven relaxation, such as replacing a property by a super-property, or a class by a super-class. Many RDF datasets are provided with an ontology or schema, and hence the RELAX operator is also a natural candidate to add to SPARQL 1.1. Flexible querying techniques have the potential to enhance users’ access to com- plex, heterogeneous datasets, by allowing the retrieval of non-exact answers to queries that are related in some way to the exact answers. In particular, users querying Linked Data may lack full knowledge of the structure of the data, its irreg- ularities, and the URIs used within it. Users might not know all the properties that are needed to express a valid query because of the complexity and heterogeneity of the data. Also, the meaning of properties can be misinterpreted which may lead to invalid assumptions when formulating a query. Moreover, the structure of the data, the URIs used and their classification, may also evolve over time.
There are many interesting directions we would like to explore in the future. We would like to measure the impact which the QueRIE relaxation process has in the quality of recommendations. Exploring a sequence-based approach is another interesting direction for the future work, but it needs a careful reconsideration of several aspects of our frame work. For instance, pure sequence information may not be sufficient to discover user similarities. Instead, we might have to consider the relative changes between queries in the sequence, e.g., the selection predicates becomes more selective as queries advance, in order to properly detect similarities. We also plan to focus on relational databases that have a form based interface. While that of the fragment based approach appears as a straightforward selection for such environments, new tests related to the formulation of session similarity, the synthesis of the proposal and their presentation arise. Finally, as we aim at developing a more generic and scalable system, we are currently working on combining alternative techniques for generating recommendations.
We have shown an approach to queryrecommendation that is based on casting this problem in an optimization framework, in which we perturb users’ query-reformulation paths to maximize the expected value of some suitable util- ity function deﬁned over search sessions. We deﬁned two utility functions Max-Last and Max-Sum which, respec- tively, formalize the goals of reaching a valuable destination or traversing many valuable nodes. We have shown that this problem is in general NP-hard, but that we can provide eﬀective and eﬃcient approximation algorithms for it, with provable performance in signiﬁcant cases. Finally, we have implemented our approximation heuristics and tested them on real test sets, also carrying out a user study that con- ﬁrms that our techniques can be used to generate query rec- ommendations that are perceived similar in quality to what users would consider more relevant to their search goals, but that at the same time bias users’ browsing along reformula- tion paths that achieve a much higher utility than without such assistance.
Despite the fact that this simple interaction mechanism has proved to be successful for searching the Web, a list of keywords is not always a good descriptor of the information needs of users. It is not always easy for users to retrieve relevant information as they cannot formulate effective queries to search engines. One reason for this is the ambiguity that arises in many terms of a language such as ambiguity aroused due to polysemous words. Queries having ambiguous terms may retrieve documents which are not relevant to user’s search because they are retrieved on the basis of page rank. On the other hand, users typically submit very short queries to the search engine, and short queries are more likely to be ambiguous. From a study of the log of a popular search engine, Jansen e t a l [ 5 ] ,
Traditional question forms square measure designed and pre-defined by developers or DBA in varied data management systems.
With the fast development of internet data and scientific databases, trendy databases become terribly massive and sophisticated.
Therefore, it is tough to
Style a collection of static question forms to satisfy varied ad-hoc information queries on those complicated databases. Query forms are designed and pre-defined by developers in data management systems. Difficult to style a collection of static question forms to satisfy varied ad-hoc info queries on complicated databases. We propose a dynamic question type system that generates the question forms per the user’s need at run time. The system provides an answer for the question interface in massive and arduous databases. This paper proposes DQF, a completely unique info question type interface that is in a position to dynamically generate question forms. The aspect of DQF is to capture a user’s preference and rank question type parts, aiding him/her to form selections. The generation of a question type is associate degree unvaried method and is guided by the user. At every iteration, the system mechanically generates ranking lists of type parts and also the user then adds the specified type parts into the question type.
physical database resource usage such as the frequency of accessing CPU and the relative position in the I/O queue.
Although goal-oriented, multi-class resource allocation is becoming a trend for DBMS resource management, most research work and current commercial products treat extremely large queries in a static and somewhat “crude” way. A popular approach is to adopt some kind of admission control mechanism to preclude large queries out of the system in advance and delay their execution until a system off-peak time. Our research investigates an approach such that not only do other queries in the system have more reasonable resource allocation, but the large queries themselves can be controlled in a more flexible and manageable way. The “utility throttling” technique used by Parekh et al.
This paper proposes a dynamic query form generation approach that helps users dynamically generate query forms. The key plan is to use a probabilistic model to rank type elements supported user preferences. We have a tendency to capture user preference mistreatment each historical queries and run-time feedback like click through. Experimental results show that the dynamic approach typically results in higher success rate and easier question forms compared with a static approach. The ranking of type elements additionally makes it easier for users to customize query forms.
KEYWORDS: Clustering, faceted search, Query facet, Page parsing, summarization.
One aspect of the query is a collection of elements that describe and summarize an important aspect of a query. Here, a facet element is usually a word or a phrase. A query can have multiple aspects that summarize the query information from different perspectives. Table 1 shows the sample facets for some queries. The facets of the "look" query concern the knowledge of watches in five unique aspects, which include brands, gender categories, support characteristics, styles and colors.Query aspects provide interesting and useful information about a query and, therefore, can be used to improve research experiences in many ways. First, we can show the faces of the query together with the original search results appropriately. Therefore, users can understand some important aspects of a query without having to go through dozens of pages.In this document, a proposed system scans to automatically identify the look related to searching for open domain queries in the Web search engine. The faces of a query are automatically extracted from the results of the main query web search without the need for further domain knowledge. Because the aspects of the consultation are good summaries of a query and are potentially useful for users to understand the query and help them explore the information, data sources are possible that allow a general multifaceted exploratory search of open domain.
We verified that the results reported in Table 2 and 3 were statistical significant by applying a paired t-test at the 0.01 level of confidence between the mean differences reported for all the possible pair of methods considered. We found that the differences reported between all the methods (e.g. Bing vs. rw-b, Bing vs. rw-kl-f, rw-b vs. rw-kl-f ) were statistical significant for all the results reported. However, the exception was for the set of adult queries for which the difference between the methods rw-kl-f and rw-kl-b were not statistical significant. Values that were not proven statistical significant (between our two random walk variations) are underlined in the result tables. Tables 4 and 5 show the results obtained for the set of AOL query reformulations in which the reformulation leads to a long click. All the methods obtained lower performance values. For instance for the children queries Bing obtains a recall of 1.8% at top 10 in contrast with the 2.1% obtained when using the first set of query pairs. Similarly the NDCG score obtained by Bing for the teenager set of queries (at top 10) is of 0.031 in the first set and 0.024 in the second. These results suggest that the problem of predicting query reformulation is harder when we are targeting reformulations that lead to long clicks. It is important to mention that even though we observed lower performance values, the ratio between our method and the two baselines were larger. For instance at top 10 the performance gain of rw-k-b in respect to rw-b was 8.5% and 10.2% in respect to Bing. Using the first set of query reformulations the performance gains were of 8.1% and 10.0% respectively. This result shows that our method performs better in the problem of predicting query suggestions that lead to long clicks which is convenient since these query suggestions have a higher likelihood of leading to relevant information.
user. Knowing where a mobile user is can mean knowing what he's doing: attending a church service or support meeting, visiting a doctor's office, buying an engagement ring, conducting non-business activities or spending an evening at the corner of the office. bar. He could reveal that he is interviewing for a new job or "out" as a participant in a gun rally or peace protest. It may mean knowing who he / she spends time with and how often. When location data is aggregated, it can reveal its habits and routines - and when it deviates from them. A 2010 survey for Microsoft in the United Kingdom, Germany, Japan, the United States and Canada found that 94% of consumers who used location-based services thought they were useful, but the same survey found that 52% privacy1. In this article, we investigate the k nearest neighbors (kNN) queries where the mobile user queries the LBS provider on k closest POIs. In general, the mobile user must submit his location to the LBS provider who then discovers and returns to the user the nearest k POIs by comparing the distances between the mobile user's location and the nearby points of interest. . This reveals the location of the mobile user to the LBS provider.
Determinism: The running time of the algorithm can be deterministic or randomized and it can be deterministic in nature.
Accuracy: This represents the error of the output of the algorithm. Some algorithms produce exact result ABSTRACT : In recent years, the Iceberg query evaluation is attracted more number of researchers, scientists, and decision makers. The reason behind this is demand of scalability and efficiency. Always researchers try to find the best methods of computation which takes limited computing resources for large databases. The Iceberg Query (IB) analysis showing that the IB queries are consuming more time than association rule formation from the datasets. So researchers are working continuously to solve the problem of the IB query evaluation in the domain of data warehousing, data mining, and information retrieval systems. As the result, many novel ideas and techniques have been generated in IB query evaluation process. In this paper, we proposed a novel algorithm to evaluate iceberg queries in order to improve performance of iceberg queries.
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6 D ISCUSSION AND C ONCLUSIONS
We have proposed a heterogeneous LQB graph as a rep- resentation of the interactive knowledge about people’s be- haviour across the physical and cyber spaces. We have high- lighted the utility of the LQB graph method in an indoor re- tail scenario, based on the analysis of a large dataset captur- ing the indoor physical and Web activity of registered WiFi users. Following an analysis on the contextual influence on people’s information and physical behaviour, we confirm the strong inter-dependencies between people’s querying, browsing and spatial behaviours, as previously suggested , , . In contrast to previous literature, we explored these interdependencies in a constrained and controlled physical environment (a shopping mall) and across all three contextual influences. To do this, we populated the locations subgraph of the LQB graph with Wi-Fi AP associations, the query subgraph with queries, and the browse graph with URL domains. We have then shown that the tripartite LQB graph successfully models the physical and Web content as- pects of context and outperforms the state-of-the-art models in location, query and Web content recommendation. The proposed LQB graph model significantly outperforms even the baselines achieved using the partial graphs as well as the more naive models.
ABSTRACT: Keyword suggestion in web search is a very important featureto be considered in today’s growing world.
It helps user toaccess the information without any prior knowledge of how toexpress in queries. The main concept of query suggestion isused to retrieve documents from the related server byconsuming less time. Platform is provided by search enginesfor users to describe their information need more precisely by using queryrecommendation. Previously there has been lot ofwork done for retrieving relevant data of users to meet theirinformation need and improving performance of searchengines. This paper reviews and compares different available methods in query log processing for information retrieval.Then conclude that Existing keyword suggestion techniques are not considering the locations of the users and the queryresults which serves as a drawback of existing systems. Thespatial factor is not considered to retrieve result. The approachbased on location aware keyword query suggestion is better tounderstand user’s interaction process with search engines tofind the appropriate information need.For this we have fragmented database according to the location and then used heap sort and SHA-256 algorithm to provide query suggestion.it help user to find appropriate query considering location of user and also system overhead will be less.
Web has a tremendous growth in terms of both content and number of users, this has led to a serious problem of information overloading in which it is difficult for users to locate authentic information in the given time. Recommender Engines have been developed to address this problem, by guiding the users through the information and helping them find the right information. Traditional Recommender Engine sought to predict the 'rating' or 'preference' that a user would give to an item or social element they had not yet considered, this model is developed from the characteristics of an item or the user's social environment. Spatially Aware Recommender Engine on the other hand produces a location-aware recommender system that uses location based ratings to produce recommendations. This project will present the design, implementation, testing and evaluation of a recommender system with the solution for Limited resource situation and cold start problem using Hybrid filtering algorithm, Lesk based algorithm and Random algorithm.
A query facet is a set of items like words or several phrases which define and review important characteristic of a query.
One single query may have numerous facets that describe the information about the query from different viewpoints. The query“visit Mumbai” has a query facet about popular hotels in Mumbai (Marine Drive, hotel Taj, Gate way of India, . . .) and a facet on travel related areas (attractions, supermarket run, dining, . . .). Query facets offer interesting as well as beneficial knowledge about any query and hence it can be used to increase search practices in numerous ways. The users can simplify their exact intent by selecting facet things. Then search results might be limited to the documents that are important to the things. A user maybe will drill down to ladies‘ watches if he is watching for a gift for his wife. These numerous groups of query facets are in definite suitable for unclear or unclear queries, such as “apple”. We might display the products of Apple Inc. in any facet as well as numerous