在週期性無線廣播中，空中扮演著儲存媒介的角色，需要新的資料組織和存取方法。由於可攜式單元（如掌上型電腦）的電力有限，所以如何去設計一個節省能源的資料組織是一個重要的議題。Imielinski等人提出了Hashing A與Hashing B這兩個以雜湊為基礎的方法，來在取得有興趣資料的過程中能節省能源。Hashing B這方法改善了Hashing A方法的目錄遺漏的現象。（若客戶端初聽頻道時，已錯過了包含有適當位移值的資料區塊，但在包含有鍵值的資料區塊之前，我們稱這個情形為目錄遺漏。）然而，如果最小溢位資料區塊和其他的溢位資料區塊相差非常大，或是一些小的溢位資料區塊出現在廣播檔案的後端，這將會使得這兩個方法的效能變得非常差。所以，在這篇論文我們提出FirstR、FirstL、AvgK和TopK這四個以多重雜湊函式為基礎的方法，來克服上述的情況。基本的想法是將含有N個邏輯資料區塊的廣播檔案，使用分割線分成幾區。分完之後，每一區可能會有不同大小的最小溢位資料區塊。因為每一區的最小溢位資料區塊可能會有不同的大小，所以在每一區中我們就能使用不同的雜湊函式來決定含有位移值的資料區塊的位置。在我們所提出來這四個方法中，其差異點在於如何決定分割線的位置。FirstR方法由右往左找尋分割線的位置，當兩相鄰的邏輯資料區塊之後溢位資料區塊相差值大於或等於1，就決定分割線的位置。FirstL方法由左往右找尋分割線的位置，當兩相鄰的邏輯資料區塊之後溢位資料區塊相差值大於或等於1，就決定分割線的位置。在AvgK方法中，我們先算出AvgD（為所有兩相鄰溢位區塊的差異值大於等於1的平均值），接下來由左往右找分割線，當兩相鄰的邏輯資料區塊之後溢位資料區塊相差值大於或等於AvgD，就決定分割線的位置。TopK方法由溢位區塊的差異值遞減順序，來決定分割線的位置。在我們的效能分析與模擬研究中，我們發現TopK方法是我們所提出的方法中效能最好的方法，所以我們拿TopK方法來和Hashing B方法做比較。由於TopK方法的雜湊函數的數量比Hashing B方法多，所以TopK方法的實際資料區塊會比Hashing B方法來得大。我們的模擬程式也考慮了這一點。經由我們的效能分析與模擬研究，即使考慮了上述資料儲存大小的因素，TopK方法的效能仍然比Hashing B來得好。TopK方法改善Hashing B方法的目錄遺漏，所以平均的存取時間被改進了很多。

In periodic wireless broadcasting, air behaves like a storage medium requiring new data organization and access methods.Due to power limit for the portable units (ex. the palmtop), how to design an energy-saving organization is a key issue.Imielinski et al. have proposed the hashing based schemes, including the Hashing A and Hashing B schemes, to save energy in the progress of getting data of interest. The Hashing B scheme improves the directory miss phenomenon in the Hashing A scheme, where the directory miss is that the client's initial probe comes before
the bucket containing his key but after the bucket which contains a proper offset. However, based on these two schemes, if the differences between the minimum overflow and the other overflows are large extremely or the small overflows appear near the rear part of the broadcast file, both schemes have a poor performance. Therefore, in this thesis, we propose four multiple-hashing-functions-based
schemes, including the FirstR, FirstL, AvgK and TopK schemes, to overcome such the situations.
The basic idea is to use cutlines to divide the
broadcast file with N logical buckets into several regions, and then each region may have
the different minimum overflow. Since the minimum overflow in each region can be different, we can have different hashing functions for those regions to determine the positions of the designated buckets.
Among the proposed schemes, the difference is how to determine the positions of the cutlines. The FirstR scheme finds those cutlines from the right end to the left whenever the difference of overflows of two adjacent logical buckets is greater than or equal to 1. The FirstL scheme finds those cutlines from the left end to the right whenever the difference of overflows of two adjacent logical buckets is greater than or equal to 1. In the AvgK scheme, we first calculate AvgD, the average of the differences of two consecutive overflows whose values are large than or equal to 1. Then we find cutlines from the left end to the right whenever the difference of
two adjacent logical buckets is greater than or equal to AvgD. The TopK determines the cutlines by considering the descending order of the differences of overflows. From our performance analysis and simulation study, the
performance of the TopK scheme is the best among the proposed schemes. Therefore, we then make a comparison between the TopK scheme and the Hashing B scheme. Since the number of the hashing functions in the TopK scheme is larger than those in the Hashing B, the physical bucket in the TopK scheme is somewhat bigger than that in the m Hashing B scheme. In our simulation, we have considered this factor as well. From our performance analysis and simulation study, we show that the performance of the TopK scheme performs better than that of the Hashing B
scheme, even though the above factor about the storage size is considered. The TopK scheme improves the directory miss in the Hashing B scheme; therefore, the average access time is
improved excellently.

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Special Features of Mobile Environments . . . . . . . . . . . . . . . . 2
1.2 Energy-Saving Data Organization . . . . . . . . . . . . . . . . . . . . 4
1.3 Hashing Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.1 Hashing A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.2 Hashing B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2. A Survey of Energy-Saving Data Organization . . . . . . . . . . . . 18
2.1 Indexing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.1.1 Access Time Optimum . . . . . . . . . . . . . . . . . . . . . . 18
2.1.2 Tuning Time Optimum . . . . . . . . . . . . . . . . . . . . . . 19
2.1.3 Flexible Indexing . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.4 (1,m) Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1.5 Index Distribution Methods . . . . . . . . . . . . . . . . . . . 22
2.2 Signature Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2.1 The Simple Signature Scheme . . . . . . . . . . . . . . . . . . 28
2.2.2 The Integrated Signature Scheme . . . . . . . . . . . . . . . . 29
2.2.3 Multi-level Signature Scheme . . . . . . . . . . . . . . . . . . 30
2.2.4 The Hybrid Index Approach . . . . . . . . . . . . . . . . . . . 31
2.3 Non-uniform Broadcast . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3. The Multiple-Hashing-Functions-Based Schemes . . . . . . . . . . 34
3.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Basic Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3 The FirstR Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4 The FirstL Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5 The AvgK Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.6 The TopK Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4. Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.1 Generation of Over ows . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2 Performance Analysis of Access Time and Tuning Time . . . . . . . . 58
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.1 A Comparison of the FirstR, FirstL, AvgK and TopK Schemes 60
4.3.2 A Comparison of the TopK and Hashing B Schemes . . . . . . 71
5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 82
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

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